U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Transl Vis Sci Technol
  • v.9(2); 2020 Feb

Introduction to Machine Learning, Neural Networks, and Deep Learning

Rene y. choi.

1 Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University (OHSU), Portland, Oregon, United States

Aaron S. Coyner

2 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States

Jayashree Kalpathy-Cramer

3 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States

Michael F. Chiang

J. peter campbell.

To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning.

A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology.

A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background.

Conclusions

Artificial intelligence has a promising future in medicine; however, many challenges remain.

Translational Relevance

The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.

Introduction

Over the past decade, artificial intelligence (AI) has become a popular subject both within and outside of the scientific community; an abundance of articles in technology and non-technology-based journals have covered the topics of machine learning (ML), deep learning (DL), and AI. 1 – 6 Yet there still remains confusion around AI, ML, and DL. The terms are highly associated, but are not interchangeable. In this review, we (attempt to) forgo technical jargon to better explain these concepts to a clinical audience.

In 1956, a group of computer scientists proposed that computers could be programmed to think and reason, “that every aspect of learning or any other feature of intelligence [could], in principle, be so precisely described that a machine [could] be made to simulate it.” 7 They described this principle as “artificial intelligence.” 7 Simply put, AI is a field focused on automating intellectual tasks normally performed by humans, and ML and DL are specific methods of achieving this goal. That is, they are within the realm of AI ( Fig. 1 ). However, AI includes approaches that do not involve any form of “learning.” For instance, the subfield known as symbolic AI focuses on hardcoding (i.e., explicitly writing) rules for every possible scenario in a particular domain of interest. These rules, written by humans, come from a priori knowledge of the particular subject and task to be completed. For example, if one were to program an algorithm to modulate room temperature of an office, he or she likely already know what temperatures are comfortable for humans to work in and would program the room to cool if temperatures rise above a specific threshold and heat if they drop below a lower threshold. Although symbolic AI is proficient at solving clearly defined logical problems, it often fails for tasks that require higher-level pattern recognition, such as speech recognition or image classification. These more complicated tasks are where ML and DL methods perform well. This review summarizes machine learning and deep learning methodology for the audience without an extensive technical computer programming background.

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f001.jpg

Umbrella of select data science techniques. Artificial intelligence (AI) falls within the realm of data science, and includes classical programming and machine learning (ML). ML contains many models and methods, including deep learning (DL) and artificial neural networks (ANN).

We conducted a literature search in PubMed for articles that were pertinent to leading artificial intelligence methods being utilized in medical research. Selection of articles was at the sole discretion of the authors. The goal of our literature search was to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today.

We found 33 articles that were pertinent to the main AI methods being used in medicine today.

Introduction to Machine Learning

ML is a field that focuses on the learning aspect of AI by developing algorithms that best represent a set of data. In contrast to classical programming ( Fig. 2 A), in which an algorithm can be explicitly coded using known features, ML uses subsets of data to generate an algorithm that may use novel or different combinations of features and weights than can be derived from first principles ( Fig. 2 B). 8 , 9 In ML, there are four commonly used learning methods, each useful for solving different tasks: supervised, unsupervised, semisupervised, and reinforcement learning. 8 – 10 To better understand these methods, they will be defined via an example of a hypothetical real estate company that specializes in predicting housing prices and features associated with those houses.

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f002.jpg

Classical programming versus machine learning paradigm. (A) In classical programming, a computer is supplied with a dataset and an algorithm. The algorithm informs the computer how to operate upon the dataset to create outputs. (B) In machine learning, a computer is supplied with a dataset and associated outputs. The computer learns and generates an algorithm that describes the relationship between the two. This algorithm can be used for inference on future datasets.

Supervised Learning

Suppose the real estate company would like to predict the price of a house based on specific features of the house. To begin, the company would first gather a dataset that contains many instances. 8 , 9 , 11 Each instance represents a singular observation of a house and associated features. Features are the recorded properties of a house that might be useful for predicting prices (e.g., total square-footage, number of floors, the presence of a yard). 8 , 9 , 11 The target is the feature to be predicted, in this case the housing price. 8 , 9 , 11 Datasets are generally split into training, validation, and testing datasets (models will always perform optimally on the data they are trained on). 8 , 9 Supervised learning uses patterns in the training dataset to map features to the target so that an algorithm can make housing price predictions on future datasets. This approach is supervised because the model infers an algorithm from feature-target pairs and is informed, by the target, whether it has predicted correctly. 8 – 10 That is, features, x , are mapped to the target, Y , by learning the mapping function, f , so that future housing prices may be approximated using the algorithm Y   =   f ( x ). The performance of the algorithm is evaluated on the test dataset, data that the algorithm has never seen before. 8 , 9 The basic steps of supervised machine learning are (1) acquire a dataset and split it into separate training, validation, and test datasets; (2) use the training and validation datasets to inform a model of the relationship between features and target; and (3) evaluate the model via the test dataset to determine how well it predicts housing prices for unseen instances. In each iteration, the performance of the algorithm on the training data is compared with the performance on the validation dataset. In this way, the algorithm is tuned by the validation set. Insofar as the validation set may differ from the test set, the performance of the algorithm may or may not generalize. This concept will be discussed further in the section on performance evaluation.

The most common supervised learning tasks are regression and classification. 8 – 10 Regression involves predicting numeric data, such as test scores, laboratory values, or prices of an item, much like the housing price example. 8 – 10 Classification, on the other hand, entails predicting to which category an example belongs. 8 – 10 Sticking with the previous example, imagine that rather than predicting exact housing prices in a fluctuating market, the real estate company would now like to predict a range of prices for which a house will likely sell, such as (0, 125K), (125K, 250K), (250K, 375K), and (375K, ∞). To accomplish this, data scientists would transform the numeric target variable into a categorical variable by binning housing prices into separate classes. These classes would be ordinal, meaning that there is a natural order associated with the categories. 9 However, if their task was to determine whether houses had wood, plastic, or metal siding, classes would be nominal; they are independent of one another and have no natural order. 9

Unsupervised Learning

In contrast to supervised learning, unsupervised learning aims to detect patterns in a dataset and categorize individual instances in the dataset to said categories. 8 – 10 These algorithms are unsupervised because the patterns that may or may not exist in a dataset are not informed by a target and are left to be determined by the algorithm. Some of the most common unsupervised learning tasks are clustering, association, and anomaly detection. 8 – 10 Clustering, as the name suggests, groups instances in a dataset into separate clusters based upon specific combinations of their features . 8 – 10 Say the real estate company now uses a clustering algorithm on its dataset and it finds three distinct clusters. Upon further investigation, it might find that the clusters represent the three separate architects responsible for designing the homes in their dataset, which is a feature that was not present in the training dataset.

Semisupervised Learning

Semisupervised learning can be thought of as the “happy medium” between supervised and unsupervised learning and is particularly useful for datasets that contain both labeled and unlabeled data (i.e., all features are present, but not all features have associated targets). 10 This situation typically arises when labeling images become time-intensive or cost-prohibitive. Semisupervised learning is often used for medical images, where a physician might label a small subset of images and use them to train a model. This model is then used to classify the rest of the unlabeled images in the dataset. The resultant labeled dataset is then used to train a working model that should, in theory, outperform unsupervised models. 10

Reinforcement Learning

Finally, reinforcement learning is the technique of training an algorithm for a specific task where no single answer is correct, but an overall outcome is desired. 9 , 10 It is arguably the closest attempt at modeling the human learning experience because it also learns from trial and error rather than data alone. 9 , 10 Although reinforcement learning is a powerful technique, its applications in medicine are currently limited and thus will be presented with a new example. Imagine one would like to train an algorithm to play the video game Super Mario Bros, where the purpose of the game is to move the character Mario from the left side of the screen to the right side in order to reach the flag pole at the end of each level while avoiding hazards such as enemies and pits. There is no correct sequence of controller inputs; there are sequences that lead to a win and those that do not. In reinforcement learning, an algorithm would be allowed to “play” on its own. It would attempt many different controller inputs and when it finally moves Mario forward (without receiving damage), the algorithm is “rewarded” (i.e., the behavior is reinforced). Through this process, the algorithm begins to learn what behavior is desired (e.g., moving forward is better than moving backward, jumping over enemies is better than running into them). Eventually, the algorithm learns how to move from start to finish. Although reinforcement has its place in the field of computer science and machine learning, it has yet to make a substantial impact in clinical medicine.

Performance Evaluation

To maximize the chance of generalizability to the performance of the algorithm on unseen data, the training dataset is usually split into a slightly smaller training dataset and a separate validation dataset. 8 , 9 Metrics used for evaluation of a model depend upon the model itself and whether it is in the training or testing phase. The validation dataset is meant to mimic the test dataset and helps data scientists tune an algorithm by identifying when a model may generalize well and work in a new population. Because the validation dataset is a small sample of the true (larger) population, it may not accurately represent the population itself due to an unknown sampling bias. Therefore, model performance and generalizability should not be assessed via validation set performance. It is conceivable that a data scientist could create a validation dataset with an unknown bias and use it to tune a model. Although the model might perform well on the validation dataset, it would likely not perform well on the much larger test dataset (i.e., it would not be a generalizable model)

Typically, model performance is monitored via some form of accuracy on the training and validation datasets during this phase. So long as the accuracy of the model on the training set ( X %) and validation set ( Y %) are increasing and converging after each training iteration, the model is considered to be learning. If both converge, but do not increase (e.g., X converges on Y at 50%), the model is not learning and may be underfit to the data, that is, it may not have learned enough of the relationship between features and targets in a way that it would be expected to work in another population. Finally, if training performance increases far more than validation set performance (e.g., the model has an accuracy of 99% on the data it was trained on, but only 80% on the validation data), the model is overfit. That is, it has learned features specific to the training dataset population at the expense of generalizability to another population. Although the validation dataset is not specifically used to train the algorithm, it is used to iteratively tune the algorithm. Therefore, the validation dataset is not necessarily a reliable indicator of model performance on unseen data. 8 , 9

Upon completion of the training phase, a data scientist has, ideally, trained a highly generalizable model; however, this must be confirmed via a separate test dataset. In the case of supervised learning, which will be the focus of this review from here on, the performance of a learned model can be evaluated in a number of ways, but is most commonly evaluated based on prediction accuracy (classification) or error and residuals (regression). 8 , 9 As previously mentioned, the test dataset contains instances of the original dataset that have not been seen by the algorithm during the training phase. If the predictive power of a model is strong on the training dataset, but poor on the test dataset, then the model is too specific to the patterns from the training data and is considered to be overfit to the training dataset. 8 , 9 That is, it has memorized patterns rather than learned a generalizable model. An underfit model, on the other hand, is one that performs poorly on both training and test datasets and has neither learned nor memorized the training dataset and still is not generalizable. 8 , 9 An ideally fitted model is one that performs strongly on both datasets, suggesting it is generalizable (i.e., it will perform well on other similar datasets). 8 , 9

With regression models, the average mean squared error (MSE) can be an indicator of model performance. 8 , 9 MSE measures how close a predicted value is to the intended target value. MSE is calculated by summing the differences between predicted values and target values, squaring the results, and dividing by the total number of instances ( MSE = 1 n ∑ i = 1 n ( y i - y ^ i ) 2 ) . 8 , 9 There are many other measures of performance for regression models that are out of the scope of this review.

For binary classification, the output of the model is a class. However, before the class designation, the probability of an instance belonging to class A or class B is determined. 8 , 9 Normally, this probability threshold is set at 0.5. A receiver operating characteristic curve evaluates a model's true positive rate (TPR; i.e., sensitivity, recall), the number of samples correctly identified as positive divided by the total number of positive samples, versus its false-positive rate (FPR; i.e., 1 - specificity), the number of samples incorrectly identified as positive divided by the total number of negative samples ( Fig. 3 ,  Fig. 4 A). 8 , 9 Similarly, the precision-recall curve evaluates a model's positive predictive value (PPV; i.e., precision), the number of samples correctly identified as positive divided by the total number of samples identified as positive, versus its recall ( Fig. 3 ,  Fig. 4 B). 8 , 9 Each curve is evaluated across the range of model probability thresholds from 1 to 0, left to right. A receiver operating characteristic curve starts at the point (FPR = 0, TPR = 0), which corresponds to a decision threshold of 1 (every sample is classified as negative, and thus there are no false or true positives). It ends at the point (FPR = 1, TPR = 1), which corresponds to a decision threshold of 0 (where every sample is classified as positive, and thus all points are either truly or falsely labeled positive). The points in between, which create the curve, are obtained by calculating the TPR and FPR for different decision thresholds between 1 and 0, trading off sensitivity (minimizing false negatives) with specificity (minimizing false positives). The area under the curve (AUC) of the receiver operating characteristics curve (AUROC) can be calculated and used as a metric for evaluating the overall performance of a classifier, assuming the classes of the dataset are balanced. If classes are not balanced, the area under the precision-recall curve (AUPR) may be a better metric of model performance because the threshold (set at 0.5 in  Fig. 4 B) may be adjusted. For example, if a dataset comprised 75% of class A and 25% of class B, the ratio between the two would be computed as the threshold (0.75). In practice, an AUROC value of 0.50 indicates a model that performs no better than chance, and an AUC of 1.00 indicates that the model performs perfectly; the higher the value of the AUC, the stronger the performance of the ML model. 8 , 9 Similarly, an AUPR value at the preset threshold indicates a model that performs no better than chance, and an AUPR value of 1.00 indicates a perfect model. 8 , 9

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f003.jpg

Sensitivity, specificity, positive predictive value, and negative predictive value. A population (dataset) is represented as circles colored blue if positive or orange if negative. The dataset is input to an algorithm that predicts each instance's class association. If an instance is correctly predicted as positive or negative, it is a true positive (TP) or true negative (TN), respectively. If an instance is incorrectly labeled positive or negative, it is a false positive (FP) or false negative (FN), respectively. (A) A model with perfect sensitivity ( ∑ T P T P + F N ) and specificity ( ∑ T N T N + F P ). (B) A model with perfect sensitivity (ability to correctly classify all positive cases), but poor specificity (ability to correctly classify all negative cases) and (C) a model with perfect specificity, but poor sensitivity. Although a model might have perfect sensitivity (B), it can have many false positives. Similarly, a model with perfect specificity (C) might have many false negatives. Therefore, it is also useful to evaluate the positive predictive value (PPV; ∑ T P T P + F P ) and the negative predictive value (NPV; ∑ T N T N + F N ). PPV and NPV are also thus dependent on the prevalence of disease in a population.

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f004.jpg

Example receiver operating characteristics and precision-recall curves. Red line : a model that performs no better than chance has an area under the curve (AUC) of the receiver operating characteristics curve (AUROC) of 0.50 or area under the precision-recall curve (AUPR) at the class ratio ( red shaded area ). Blue line : a model that performs better than chance, but not perfectly, will have an AUC between 0.50 and 1.00 ( blue + red shaded areas ). Green line : a model that performs perfectly has an AUC of 1.00 ( red + blue + green shaded areas ).

Classic Machine Learning Methods

There are many machine learning algorithms used in medicine. Described next are some of the most popular to date.

Linear Regression

Linear regression is arguably the simplest ML algorithm. The main idea behind regression analysis is to specify a relationship between one or more numeric features and a single numeric target. 8 , 9 Linear regression is an analysis technique used to solve a regression problem by using a straight line to describe a dataset. Univariate linear regression, a regression problem where only a single feature is used for predicting a target value, can be represented in a slope-intercept form: y   =   ax   +   b . 8 , 9 Here, a is a weight describing the slope, which describes how much a line increases on the y-axis for each increase in x . The intercept, b , describes the point where the line intercepts the y-axis. Linear regression models a dataset using this slope-intercept form, where the machine's task is to identify values of a and b such that the determined line is best able to relate the supplied values of x values to the values of y . Multivariate linear regression is similar; however, there are multiple weights in the algorithm, each describing to what degree each feature influences the target. 8 , 9

In practice, there is rarely a single function that fits a dataset perfectly. To measure the error associated with a fit, the residuals are measured. Conceptually, residuals are the vertical distances between predicted values, y ^ , and actual values, y . In machine learning, the cost function is a calculus derived term that aims to minimize errors associated with a model. 8 , 9 The process of minimizing the cost function involves an iterative optimization algorithm known as gradient descent, of which the mathematical calculations involved are outside the scope of this article. 8 , 9 , 12 In linear regression, the cost function is the previously described MSE. Minimizing this function often obtains estimates of a and b that best model a dataset. All model-based learning algorithms have a cost function, and the goal is to minimize this function to find the best-fit model. 8 , 9

Logistic Regression

Logistic regression is a classification algorithm where the goal is to find a relationship between features and the probability of a particular outcome. Rather than using the straight line produced by linear regression to estimate class probability, logistic regression uses a sigmoidal curve to estimate class probability ( Fig. 5 ). This curve is determined by the sigmoid function, y = 1 1 + e - x , which produces an S-shaped curve that converts discrete or continuous numeric features ( x ) into a single numerical value ( y ) between 0 and 1. 8 , 9 The major advantage of this method is that probabilities are bounded between 0 and 1 (i.e., probabilities cannot be negative or greater than 1). It can be either binomial, where there are only two possible outcomes, or multinomial, where there can be three or more possible outcomes. 8 , 9

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f005.jpg

Example class probability prediction using linear and logistic regression. Presented are linear ( blue line ) and logistic ( red line ) regression models for predicting the probability of various samples ( gray circles ) as belonging to a particular class using a single variable, variable X , which ranges from -10 to 10. With logistic regression, variable X is transformed into class probabilities that are bounded between 0 and 1 using the sigmoid function. Simple linear regression attempts to estimate class probabilities, but is not bounded between 0 and 1; thus, it breaks a fundamental law of probability that does not allow for negative probabilities or those greater than 1.

Decision Trees and Random Forests

A decision tree is a supervised learning technique, primarily used for classification tasks, but can also be used for regression. 8 , 9 A decision tree begins with a root node, the first decision point for splitting the dataset, and contains a single feature that best splits the data into their respective classes ( Fig. 6 ). 8 , 9 Each split has an edge that connects either to a new decision node that contains another feature to further split the data into homogenous groups or to a terminal node that predicts the class. This process of separating data into two binary partitions is known as recursive partitioning . 8 , 9 A random forest is an extension of this method, known as an ensemble method, that produces multiple decision trees. 8 , 9 Rather than using every feature to create every decision tree in a random forest, a subsample of features are used to create each decision tree. Trees then predict a class outcome, and the majority vote among trees is used as the model's final class prediction. 8 , 9

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f006.jpg

Structure of a decision tree. Splitting of the dataset begins at the root node. Each split connects to either another decision node, which results in further splitting of the data, or a terminal node that predicts the class of the data.

Classic Machine Learning in Ophthalmology

Although DL has become a highly popular technique in ophthalmology, there are a multitude of examples of classic ML algorithms being used in the field. Simple linear models have been used to predict patients who would develop advanced age-related macular degeneration and to discern which factors separate patients into who will respond to anti-vascular endothelial growth factor treatment versus those who will not. 13 – 16 Random forest algorithms have been used to discover features that are most predictive of progression to geographic atrophy in age-related macular degeneration and find prognostic features for visual acuity outcomes of intravitreal anti-vascular endothelial growth factor treatment. 17 , 18 Random forest classifiers have also been applied to diagnose and grade cataracts from ultrasound images, as well as identify patients with glaucoma based on retinal nerve fiber layer and visual field data. 19 , 20

Neural Networks and Deep Learning

An artificial neural network (ANN) is a machine learning algorithm inspired by biological neural networks. 8 , 9 , 21 Each ANN contains nodes (analogous to cell bodies) that communicate with other nodes via connections (analogous to axons and dendrites). Much in the way synapses between neurons are strengthened when their neurons have correlated outputs in a biological neural network (the Hebbian theory postulates that “nerves that fire together, wire together”), connections between nodes in an ANN are weighted based upon their ability to provide a desired outcome. 8 , 9 , 21

Feedforward Neural Networks

A perceptron is a machine learning algorithm that takes in a series of features and their targets as input and attempts to find a line, plane, or hyperplane that separates the classes in a two-, three-, or hyper-dimensional space, respectively. 9 , 22 , 23 These features are transformed using the sigmoid function ( Fig. 7 A). Thus, this method is similar to logistic regression; however, it only provides class associations, and not the probability of an instance belonging to a class.

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f007.jpg

Components of a neural network. (A) The basis of an artificial neural network, the perceptron. This algorithm uses the sigmoid function to scale and transform multiple inputs into a single output ranging from 0 to 1. (B) An artificial neural network connects multiple perceptron units, so that the output of one unit is used as input to another. Additionally, these units are not limited to using the sigmoid activation function. (C) Examples of four different activation functions: sigmoid, hyperbolic tangent, identity, and rectified linear unit. The sigmoid scales inputs between 0 and 1 using an S-shaped curved. Similarly, the hyperbolic tangent function uses an S-shaped curve, but scales inputs between -1 and 1. The identity function can multiply its input by any number to produce a linear output. The rectified linear unit is similar to the identity function, however all inputs < 0 are given an output value of 0. There are other activation functions outside of these, but these are arguably.

When multiple perceptrons are connected, the model is referred to as a multilayer perceptron algorithm or an ANN. Commonly, ANNs contain a layer of input nodes, a layer of output nodes, and a number of “hidden layers” between the two. 9 In simple ANNs, there exists an input layer between zero and three hidden layers and an output layer, whereas deep neural networks contain tens or even hundreds of hidden layers. 9 , 24 For most tasks, ANNs feed information forward. This is known as a feedforward neural network, meaning information from each node in the previous layer is passed to each node in the next layer, transformed, and passed forward to each node in the next layer ( Fig. 7 B). 9 In recurrent neural networks, which are out of the scope of this paper, information can be passed between nodes within a layer or to previous layers, where their output is operated on and fed forward once again. 22

Each layer in an ANN can contain any number of nodes; however, the number of nodes in the output layer typically corresponds to the number of classes being predicted if the goal is multiclass classification, a single node with a sigmoidal activation for binary classification, or a linear activation function if the goal is regression. 9 , 24 These activation functions simply transform a node's input into a desired output ( Fig. 7 C). Each node in an ANN contains an activation function (not just the output layer;  Fig. 7 B). These activation functions, although not always linear, do not have to be complex. For instance, the rectified linear unit applies a linear transformation to inputs ≥ 0, and sets inputs < 0 to 0. 25 It follows that as inputs proceed through an ANN, they are progressively modified at each layer so that at the final layer they no longer resemble their original state. However, this final representation of the input is, in theory, more predictive of the specified outcome.

Convolutional Neural Networks

For image recognition tasks, each input into a feedforward ANN corresponds to a pixel in the image. However, this is not ideal because there are no connections between nodes in a layer. In practice, this means that the spatial context of features in the image are lost. 24 , 26 , 27 In other words, pixels that are close to one another in an image are likely more correlated than pixels on opposite sides of the image, but a feedforward ANN does not take this into account.

A convolutional neural network (CNN) is a special case of the ANN that overcomes this issue by preserving the spatial relationship between pixels in an image. 24 , 26 , 27 Rather than using single pixels as input, a CNN feeds patches of an image to specific nodes in the next layer of nodes (rather than all nodes), thereby preserving the spatial context from which a feature was extracted. 9 , 24 , 26 , 27 These patches of nodes learn to extract specific features and are known as convolutional filters.

Convolutions are widely used in the realm of image processing, and are often used to blur or sharpen images, or for other tasks such as edge detection. 28 A visible-light digital image is simply a single matrix if the image is grayscale or three stacked matrices if the image is color (red, green, and blue color channels). 28 These matrices contain values, typically between 0 and 255, that represent pixels in the image and the intensity of each color channel at each pixel. 28 A convolutional filter is a much smaller matrix that is typically square and range in size from 2 × 2 to 9 × 9. 28 This filter is passed over the original image and, at each position, element-wise matrix multiplication is performed ( Fig. 8 ). 28 The output of this convolution is mapped to a new matrix (a feature map) that contains values corresponding to whether or not the convolutional filter detected a feature of interest. 24 , 26 – 29

An external file that holds a picture, illustration, etc.
Object name is tvst-9-2-14-f008.jpg

Example of a digital image convolved with a filter. The image ( left ) is transformed into the feature map ( right ) via a convolutional filter ( center ). The convolutional filter is designed to locate diagonal lines running from top left to bottom right of the image. The filter passes over the image in a specified manner and each element in the image ( red ) is multiplied by the corresponding element in the convolutional filter ( blue ). The summation of these elements ( orange ) is output into a new matrix that reports the presence of a diagonal line. The feature map indicates 2 when the specified diagonal line is found, 1 if a portion of it is found, and 0 if none of it is found.

In CNNs, filters are trained to extract specific features from images (e.g., vertical lines, U-shaped objects,) and mark their location on the feature map. 26 , 27 A deep CNN then uses the feature map as input for the next layer, which uses new filters to create another new feature map. 24 , 26 , 27 This can continue for many layers and, as it continues, the extracted features become abstract, but highly useful for prediction. The final features maps are then compressed from their square representations and input to a feedforward ANN, where classification of the image based on the extracted features and textures can occur. 24 , 26 , 27 This process is referred to as DL. 24

Aside from image classification tasks, DL has shown promise for image segmentation tasks. 1 , 30 , 31 Rather than classifying images as a whole, this method aims to identify objects within an image. To accomplish this task, DL classifies individual pixels given surrounding pixel information. For example, in diabetic retinopathy, a segmentation algorithm might segment (outline) the retinal vasculature by assigning probabilities to individual pixels as belonging to a retinal blood vessel or not belonging to a retinal blood vessel. A similar method for breast cancer detection could mark pixels as belonging to a mass or not belonging to a mass, and the output image could be provided to a radiologist for further review.

Deep Learning in Ophthalmology

The popularity for DL has especially risen in the field of ophthalmology for image-based diagnostic systems. On the simpler end of visual interpretation tasks, Coyner et al. devised a DL system for automated assessment of retinal fundus image quality with an output of “acceptable” or “not acceptable” based on multiple graded expert labels. 3 Presumably, the network learned that the retinal vasculature must be easily distinguishable for an image to be deemed acceptable. In a more complex task, Gulshan et al. demonstrated that DL could classify diabetic retinopathy, in agreement with the Early Treatment for Diabetic Retinopathy Study scale, using only retinal fundus images as input and the consensus diagnoses of multiple clinicians as the “class labels.” 2 The presence of features such as microaneurysms, intraretinal hemorrhages, or neovascularization were not supplied to the DL method as signs of diabetic retinopathy. Rather, the DL model either learned these features or learned novel features that aid in the diagnosis of diabetic retinopathy. Further, Brown et al. trained a similar DL network for the diagnosis of plus disease in retinopathy of prematurity. First, an algorithm was trained to segment retinal vasculature into binary vessel maps. Then another DL algorithm was trained to examine the vessel maps and conclude whether the vasculature appeared normal or abnormal. 1 This network, too, performs on par or better than most experts in the field. One of the most impressive examples of DL in ophthalmology was conducted by De Fauw et al. Using three-dimensional optical coherence tomography images, a DL framework was trained to not only detect a single disease, but more than 50 common retinal diseases. 6

Challenges with DL Models

In recent years, DL has become a hot topic within the field of medicine given the digital availability of information; however, many challenges still exist. DL is limited by the quantity and quality of data used to train the model. It is difficult to estimate how much data are necessary to sufficiently and reliably train DL systems because it depends both on the quality of the input training data as well as the complexity of the task. Typically, thousands of training examples are required to create a model that is both accurate and generalizable. Thus, developing models for identification of rare diseases, where large datasets may not be readily available, is especially challenging. On the other hand, although one might assume that more data will always lead to better models, if the quality of the training data is imprecise, mislabeled, or somehow systematically different than the test population, training on very large datasets may result in models that do not perform well in real-world scenarios. Furthermore, there is an implicit assumption that datasets are accurately labeled by human graders. Unfortunately, this is often not the case, and noisy and/or missing labels are often a bane for data scientists.

DL methods also suffer from the “black box” problem: input is supplied to the algorithm and an output emerges, but it is not exactly clear what features were identified or how they informed the model output. 29 , 32 , 33 In contrast, simple linear algorithms, although not always as powerful as DL, are easily interpretable. The computed weights for each feature are supplied upon completion of the training process, which allow for one to interrogate exactly how the model works and possibly discover important predictors that may be useful for prevention of a disease. With deep learning, a complex series of matrix multiplication and abstract filters makes interpretability significantly more challenging. 29 , 32 , 33 Activation maps, or heatmaps, are methods that attempt to address the “black box” issue by highlighting areas of images that highlight regions of an image that “fire together” with the output classification label. 29 , 32 , 33 Unfortunately, these methods still require human interpretation, as they are often not examined critically (examples are cherry picked for publication, highly subject to confirmation bias, etc.), and thus this remains an active area of research. For instance, if a DL model classifies a fundus image as having proliferative diabetic retinopathy, a heatmap will highlight feature areas on that fundus image that contributed to the decision of being classified as having proliferative diabetic retinopathy. It is up to the physician to interpret whether these DL model identified features are the same features the physician would use to diagnose the disease, and the implications of such findings.

AI methods have shown to be a promising tool in the field of medicine. Recent work has demonstrated that these methods can develop effective diagnostic and predictive tools to identify various diseases. In the future, AI-based programs may become an integral part of patients’ clinic visits with their ability to assist in diagnosis and management of various diseases. Physicians should take an active approach to understand the theories behind AI and its utility in medicine with the goal of providing optimal patient care.

Acknowledgments

This project was supported by grants R01EY19474, K12 EY027720, and P30EY10572 from the National Institutes of Health; SCH-1622679, SCH-1622542, and SCH-1622536 from the National Science Foundation; and by unrestricted departmental funding and a Career Development Award (JPC) from Research to Prevent Blindness.

Disclosure: R.Y. Choi, None; A.S. Coyner, None; J. Kalpathy-Cramer, None; M.F. Chiang, None; J.P. Campbell, None

  • Survey Paper
  • Open access
  • Published: 31 March 2021

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

  • Laith Alzubaidi   ORCID: orcid.org/0000-0002-7296-5413 1 , 5 ,
  • Jinglan Zhang 1 ,
  • Amjad J. Humaidi 2 ,
  • Ayad Al-Dujaili 3 ,
  • Ye Duan 4 ,
  • Omran Al-Shamma 5 ,
  • J. Santamaría 6 ,
  • Mohammed A. Fadhel 7 ,
  • Muthana Al-Amidie 4 &
  • Laith Farhan 8  

Journal of Big Data volume  8 , Article number:  53 ( 2021 ) Cite this article

362k Accesses

1930 Citations

37 Altmetric

Metrics details

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Introduction

Recently, machine learning (ML) has become very widespread in research and has been incorporated in a variety of applications, including text mining, spam detection, video recommendation, image classification, and multimedia concept retrieval [ 1 , 2 , 3 , 4 , 5 , 6 ]. Among the different ML algorithms, deep learning (DL) is very commonly employed in these applications [ 7 , 8 , 9 ]. Another name for DL is representation learning (RL). The continuing appearance of novel studies in the fields of deep and distributed learning is due to both the unpredictable growth in the ability to obtain data and the amazing progress made in the hardware technologies, e.g. High Performance Computing (HPC) [ 10 ].

DL is derived from the conventional neural network but considerably outperforms its predecessors. Moreover, DL employs transformations and graph technologies simultaneously in order to build up multi-layer learning models. The most recently developed DL techniques have obtained good outstanding performance across a variety of applications, including audio and speech processing, visual data processing, natural language processing (NLP), among others [ 11 , 12 , 13 , 14 ].

Usually, the effectiveness of an ML algorithm is highly dependent on the integrity of the input-data representation. It has been shown that a suitable data representation provides an improved performance when compared to a poor data representation. Thus, a significant research trend in ML for many years has been feature engineering, which has informed numerous research studies. This approach aims at constructing features from raw data. In addition, it is extremely field-specific and frequently requires sizable human effort. For instance, several types of features were introduced and compared in the computer vision context, such as, histogram of oriented gradients (HOG) [ 15 ], scale-invariant feature transform (SIFT) [ 16 ], and bag of words (BoW) [ 17 ]. As soon as a novel feature is introduced and is found to perform well, it becomes a new research direction that is pursued over multiple decades.

Relatively speaking, feature extraction is achieved in an automatic way throughout the DL algorithms. This encourages researchers to extract discriminative features using the smallest possible amount of human effort and field knowledge [ 18 ]. These algorithms have a multi-layer data representation architecture, in which the first layers extract the low-level features while the last layers extract the high-level features. Note that artificial intelligence (AI) originally inspired this type of architecture, which simulates the process that occurs in core sensorial regions within the human brain. Using different scenes, the human brain can automatically extract data representation. More specifically, the output of this process is the classified objects, while the received scene information represents the input. This process simulates the working methodology of the human brain. Thus, it emphasizes the main benefit of DL.

In the field of ML, DL, due to its considerable success, is currently one of the most prominent research trends. In this paper, an overview of DL is presented that adopts various perspectives such as the main concepts, architectures, challenges, applications, computational tools and evolution matrix. Convolutional neural network (CNN) is one of the most popular and used of DL networks [ 19 , 20 ]. Because of CNN, DL is very popular nowadays. The main advantage of CNN compared to its predecessors is that it automatically detects the significant features without any human supervision which made it the most used. Therefore, we have dug in deep with CNN by presenting the main components of it. Furthermore, we have elaborated in detail the most common CNN architectures, starting with the AlexNet network and ending with the High-Resolution network (HR.Net).

Several published DL review papers have been presented in the last few years. However, all of them have only been addressed one side focusing on one application or topic such as the review of CNN architectures [ 21 ], DL for classification of plant diseases [ 22 ], DL for object detection [ 23 ], DL applications in medical image analysis [ 24 ], and etc. Although these reviews present good topics, they do not provide a full understanding of DL topics such as concepts, detailed research gaps, computational tools, and DL applications. First, It is required to understand DL aspects including concepts, challenges, and applications then going deep in the applications. To achieve that, it requires extensive time and a large number of research papers to learn about DL including research gaps and applications. Therefore, we propose a deep review of DL to provide a more suitable starting point from which to develop a full understanding of DL from one review paper. The motivation behinds our review was to cover the most important aspect of DL including open challenges, applications, and computational tools perspective. Furthermore, our review can be the first step towards other DL topics.

The main aim of this review is to present the most important aspects of DL to make it easy for researchers and students to have a clear image of DL from single review paper. This review will further advance DL research by helping people discover more about recent developments in the field. Researchers would be allowed to decide the more suitable direction of work to be taken in order to provide more accurate alternatives to the field. Our contributions are outlined as follows:

This is the first review that almost provides a deep survey of the most important aspects of deep learning. This review helps researchers and students to have a good understanding from one paper.

We explain CNN in deep which the most popular deep learning algorithm by describing the concepts, theory, and state-of-the-art architectures.

We review current challenges (limitations) of Deep Learning including lack of training data, Imbalanced Data, Interpretability of data, Uncertainty scaling, Catastrophic forgetting, Model compression, Overfitting, Vanishing gradient problem, Exploding Gradient Problem, and Underspecification. We additionally discuss the proposed solutions tackling these issues.

We provide an exhaustive list of medical imaging applications with deep learning by categorizing them based on the tasks by starting with classification and ending with registration.

We discuss the computational approaches (CPU, GPU, FPGA) by comparing the influence of each tool on deep learning algorithms.

The rest of the paper is organized as follows: “ Survey methodology ” section describes The survey methodology. “ Background ” section presents the background. “ Classification of DL approaches ” section defines the classification of DL approaches. “ Types of DL networks ” section displays types of DL networks. “ CNN architectures ” section shows CNN Architectures. “ Challenges (limitations) of deep learning and alternate solutions ” section details the challenges of DL and alternate solutions. “ Applications of deep learning ” section outlines the applications of DL. “ Computational approaches ” section explains the influence of computational approaches (CPU, GPU, FPGA) on DL. “ Evaluation metrics ” section presents the evaluation metrics. “ Frameworks and datasets ” section lists frameworks and datasets. “ Summary and conclusion ” section presents the summary and conclusion.

Survey methodology

We have reviewed the significant research papers in the field published during 2010–2020, mainly from the years of 2020 and 2019 with some papers from 2021. The main focus was papers from the most reputed publishers such as IEEE, Elsevier, MDPI, Nature, ACM, and Springer. Some papers have been selected from ArXiv. We have reviewed more than 300 papers on various DL topics. There are 108 papers from the year 2020, 76 papers from the year 2019, and 48 papers from the year 2018. This indicates that this review focused on the latest publications in the field of DL. The selected papers were analyzed and reviewed to (1) list and define the DL approaches and network types, (2) list and explain CNN architectures, (3) present the challenges of DL and suggest the alternate solutions, (4) assess the applications of DL, (5) assess computational approaches. The most keywords used for search criteria for this review paper are (“Deep Learning”), (“Machine Learning”), (“Convolution Neural Network”), (“Deep Learning” AND “Architectures”), ((“Deep Learning”) AND (“Image”) AND (“detection” OR “classification” OR “segmentation” OR “Localization”)), (“Deep Learning” AND “detection” OR “classification” OR “segmentation” OR “Localization”), (“Deep Learning” AND “CPU” OR “GPU” OR “FPGA”), (“Deep Learning” AND “Transfer Learning”), (“Deep Learning” AND “Imbalanced Data”), (“Deep Learning” AND “Interpretability of data”), (“Deep Learning” AND “Overfitting”), (“Deep Learning” AND “Underspecification”). Figure  1 shows our search structure of the survey paper. Table  1 presents the details of some of the journals that have been cited in this review paper.

figure 1

Search framework

This section will present a background of DL. We begin with a quick introduction to DL, followed by the difference between DL and ML. We then show the situations that require DL. Finally, we present the reasons for applying DL.

DL, a subset of ML (Fig.  2 ), is inspired by the information processing patterns found in the human brain. DL does not require any human-designed rules to operate; rather, it uses a large amount of data to map the given input to specific labels. DL is designed using numerous layers of algorithms (artificial neural networks, or ANNs), each of which provides a different interpretation of the data that has been fed to them [ 18 , 25 ].

figure 2

Deep learning family

Achieving the classification task using conventional ML techniques requires several sequential steps, specifically pre-processing, feature extraction, wise feature selection, learning, and classification. Furthermore, feature selection has a great impact on the performance of ML techniques. Biased feature selection may lead to incorrect discrimination between classes. Conversely, DL has the ability to automate the learning of feature sets for several tasks, unlike conventional ML methods [ 18 , 26 ]. DL enables learning and classification to be achieved in a single shot (Fig.  3 ). DL has become an incredibly popular type of ML algorithm in recent years due to the huge growth and evolution of the field of big data [ 27 , 28 ]. It is still in continuous development regarding novel performance for several ML tasks [ 22 , 29 , 30 , 31 ] and has simplified the improvement of many learning fields [ 32 , 33 ], such as image super-resolution [ 34 ], object detection [ 35 , 36 ], and image recognition [ 30 , 37 ]. Recently, DL performance has come to exceed human performance on tasks such as image classification (Fig.  4 ).

figure 3

The difference between deep learning and traditional machine learning

figure 4

Deep learning performance compared to human

Nearly all scientific fields have felt the impact of this technology. Most industries and businesses have already been disrupted and transformed through the use of DL. The leading technology and economy-focused companies around the world are in a race to improve DL. Even now, human-level performance and capability cannot exceed that the performance of DL in many areas, such as predicting the time taken to make car deliveries, decisions to certify loan requests, and predicting movie ratings [ 38 ]. The winners of the 2019 “Nobel Prize” in computing, also known as the Turing Award, were three pioneers in the field of DL (Yann LeCun, Geoffrey Hinton, and Yoshua Bengio) [ 39 ]. Although a large number of goals have been achieved, there is further progress to be made in the DL context. In fact, DL has the ability to enhance human lives by providing additional accuracy in diagnosis, including estimating natural disasters [ 40 ], the discovery of new drugs [ 41 ], and cancer diagnosis [ 42 , 43 , 44 ]. Esteva et al. [ 45 ] found that a DL network has the same ability to diagnose the disease as twenty-one board-certified dermatologists using 129,450 images of 2032 diseases. Furthermore, in grading prostate cancer, US board-certified general pathologists achieved an average accuracy of 61%, while the Google AI [ 44 ] outperformed these specialists by achieving an average accuracy of 70%. In 2020, DL is playing an increasingly vital role in early diagnosis of the novel coronavirus (COVID-19) [ 29 , 46 , 47 , 48 ]. DL has become the main tool in many hospitals around the world for automatic COVID-19 classification and detection using chest X-ray images or other types of images. We end this section by the saying of AI pioneer Geoffrey Hinton “Deep learning is going to be able to do everything”.

When to apply deep learning

Machine intelligence is useful in many situations which is equal or better than human experts in some cases [ 49 , 50 , 51 , 52 ], meaning that DL can be a solution to the following problems:

Cases where human experts are not available.

Cases where humans are unable to explain decisions made using their expertise (language understanding, medical decisions, and speech recognition).

Cases where the problem solution updates over time (price prediction, stock preference, weather prediction, and tracking).

Cases where solutions require adaptation based on specific cases (personalization, biometrics).

Cases where size of the problem is extremely large and exceeds our inadequate reasoning abilities (sentiment analysis, matching ads to Facebook, calculation webpage ranks).

Why deep learning?

Several performance features may answer this question, e.g

Universal Learning Approach: Because DL has the ability to perform in approximately all application domains, it is sometimes referred to as universal learning.

Robustness: In general, precisely designed features are not required in DL techniques. Instead, the optimized features are learned in an automated fashion related to the task under consideration. Thus, robustness to the usual changes of the input data is attained.

Generalization: Different data types or different applications can use the same DL technique, an approach frequently referred to as transfer learning (TL) which explained in the latter section. Furthermore, it is a useful approach in problems where data is insufficient.

Scalability: DL is highly scalable. ResNet [ 37 ], which was invented by Microsoft, comprises 1202 layers and is frequently applied at a supercomputing scale. Lawrence Livermore National Laboratory (LLNL), a large enterprise working on evolving frameworks for networks, adopted a similar approach, where thousands of nodes can be implemented [ 53 ].

Classification of DL approaches

DL techniques are classified into three major categories: unsupervised, partially supervised (semi-supervised) and supervised. Furthermore, deep reinforcement learning (DRL), also known as RL, is another type of learning technique, which is mostly considered to fall into the category of partially supervised (and occasionally unsupervised) learning techniques.

Deep supervised learning

Deep semi-supervised learning.

In this technique, the learning process is based on semi-labeled datasets. Occasionally, generative adversarial networks (GANs) and DRL are employed in the same way as this technique. In addition, RNNs, which include GRUs and LSTMs, are also employed for partially supervised learning. One of the advantages of this technique is to minimize the amount of labeled data needed. On other the hand, One of the disadvantages of this technique is irrelevant input feature present training data could furnish incorrect decisions. Text document classifier is one of the most popular example of an application of semi-supervised learning. Due to difficulty of obtaining a large amount of labeled text documents, semi-supervised learning is ideal for text document classification task.

Deep unsupervised learning

This technique makes it possible to implement the learning process in the absence of available labeled data (i.e. no labels are required). Here, the agent learns the significant features or interior representation required to discover the unidentified structure or relationships in the input data. Techniques of generative networks, dimensionality reduction and clustering are frequently counted within the category of unsupervised learning. Several members of the DL family have performed well on non-linear dimensionality reduction and clustering tasks; these include restricted Boltzmann machines, auto-encoders and GANs as the most recently developed techniques. Moreover, RNNs, which include GRUs and LSTM approaches, have also been employed for unsupervised learning in a wide range of applications. The main disadvantages of unsupervised learning are unable to provide accurate information concerning data sorting and computationally complex. One of the most popular unsupervised learning approaches is clustering [ 54 ].

Deep reinforcement learning

For solving a task, the selection of the type of reinforcement learning that needs to be performed is based on the space or the scope of the problem. For example, DRL is the best way for problems involving many parameters to be optimized. By contrast, derivative-free reinforcement learning is a technique that performs well for problems with limited parameters. Some of the applications of reinforcement learning are business strategy planning and robotics for industrial automation. The main drawback of Reinforcement Learning is that parameters may influence the speed of learning. Here are the main motivations for utilizing Reinforcement Learning:

It assists you to identify which action produces the highest reward over a longer period.

It assists you to discover which situation requires action.

It also enables it to figure out the best approach for reaching large rewards.

Reinforcement Learning also gives the learning agent a reward function.

Reinforcement Learning can’t utilize in all the situation such as:

In case there is sufficient data to resolve the issue with supervised learning techniques.

Reinforcement Learning is computing-heavy and time-consuming. Specially when the workspace is large.

Types of DL networks

The most famous types of deep learning networks are discussed in this section: these include recursive neural networks (RvNNs), RNNs, and CNNs. RvNNs and RNNs were briefly explained in this section while CNNs were explained in deep due to the importance of this type. Furthermore, it is the most used in several applications among other networks.

Recursive neural networks

RvNN can achieve predictions in a hierarchical structure also classify the outputs utilizing compositional vectors [ 57 ]. Recursive auto-associative memory (RAAM) [ 58 ] is the primary inspiration for the RvNN development. The RvNN architecture is generated for processing objects, which have randomly shaped structures like graphs or trees. This approach generates a fixed-width distributed representation from a variable-size recursive-data structure. The network is trained using an introduced back-propagation through structure (BTS) learning system [ 58 ]. The BTS system tracks the same technique as the general-back propagation algorithm and has the ability to support a treelike structure. Auto-association trains the network to regenerate the input-layer pattern at the output layer. RvNN is highly effective in the NLP context. Socher et al. [ 59 ] introduced RvNN architecture designed to process inputs from a variety of modalities. These authors demonstrate two applications for classifying natural language sentences: cases where each sentence is split into words and nature images, and cases where each image is separated into various segments of interest. RvNN computes a likely pair of scores for merging and constructs a syntactic tree. Furthermore, RvNN calculates a score related to the merge plausibility for every pair of units. Next, the pair with the largest score is merged within a composition vector. Following every merge, RvNN generates (a) a larger area of numerous units, (b) a compositional vector of the area, and (c) a label for the class (for instance, a noun phrase will become the class label for the new area if two units are noun words). The compositional vector for the entire area is the root of the RvNN tree structure. An example RvNN tree is shown in Fig.  5 . RvNN has been employed in several applications [ 60 , 61 , 62 ].

figure 5

An example of RvNN tree

Recurrent neural networks

RNNs are a commonly employed and familiar algorithm in the discipline of DL [ 63 , 64 , 65 ]. RNN is mainly applied in the area of speech processing and NLP contexts [ 66 , 67 ]. Unlike conventional networks, RNN uses sequential data in the network. Since the embedded structure in the sequence of the data delivers valuable information, this feature is fundamental to a range of different applications. For instance, it is important to understand the context of the sentence in order to determine the meaning of a specific word in it. Thus, it is possible to consider the RNN as a unit of short-term memory, where x represents the input layer, y is the output layer, and s represents the state (hidden) layer. For a given input sequence, a typical unfolded RNN diagram is illustrated in Fig.  6 . Pascanu et al. [ 68 ] introduced three different types of deep RNN techniques, namely “Hidden-to-Hidden”, “Hidden-to-Output”, and “Input-to-Hidden”. A deep RNN is introduced that lessens the learning difficulty in the deep network and brings the benefits of a deeper RNN based on these three techniques.

figure 6

Typical unfolded RNN diagram

However, RNN’s sensitivity to the exploding gradient and vanishing problems represent one of the main issues with this approach [ 69 ]. More specifically, during the training process, the reduplications of several large or small derivatives may cause the gradients to exponentially explode or decay. With the entrance of new inputs, the network stops thinking about the initial ones; therefore, this sensitivity decays over time. Furthermore, this issue can be handled using LSTM [ 70 ]. This approach offers recurrent connections to memory blocks in the network. Every memory block contains a number of memory cells, which have the ability to store the temporal states of the network. In addition, it contains gated units for controlling the flow of information. In very deep networks [ 37 ], residual connections also have the ability to considerably reduce the impact of the vanishing gradient issue which explained in later sections. CNN is considered to be more powerful than RNN. RNN includes less feature compatibility when compared to CNN.

Convolutional neural networks

In the field of DL, the CNN is the most famous and commonly employed algorithm [ 30 , 71 , 72 , 73 , 74 , 75 ]. The main benefit of CNN compared to its predecessors is that it automatically identifies the relevant features without any human supervision [ 76 ]. CNNs have been extensively applied in a range of different fields, including computer vision [ 77 ], speech processing [ 78 ], Face Recognition [ 79 ], etc. The structure of CNNs was inspired by neurons in human and animal brains, similar to a conventional neural network. More specifically, in a cat’s brain, a complex sequence of cells forms the visual cortex; this sequence is simulated by the CNN [ 80 ]. Goodfellow et al. [ 28 ] identified three key benefits of the CNN: equivalent representations, sparse interactions, and parameter sharing. Unlike conventional fully connected (FC) networks, shared weights and local connections in the CNN are employed to make full use of 2D input-data structures like image signals. This operation utilizes an extremely small number of parameters, which both simplifies the training process and speeds up the network. This is the same as in the visual cortex cells. Notably, only small regions of a scene are sensed by these cells rather than the whole scene (i.e., these cells spatially extract the local correlation available in the input, like local filters over the input).

A commonly used type of CNN, which is similar to the multi-layer perceptron (MLP), consists of numerous convolution layers preceding sub-sampling (pooling) layers, while the ending layers are FC layers. An example of CNN architecture for image classification is illustrated in Fig.  7 .

figure 7

An example of CNN architecture for image classification

The input x of each layer in a CNN model is organized in three dimensions: height, width, and depth, or \(m \times m \times r\) , where the height (m) is equal to the width. The depth is also referred to as the channel number. For example, in an RGB image, the depth (r) is equal to three. Several kernels (filters) available in each convolutional layer are denoted by k and also have three dimensions ( \(n \times n \times q\) ), similar to the input image; here, however, n must be smaller than m , while q is either equal to or smaller than r . In addition, the kernels are the basis of the local connections, which share similar parameters (bias \(b^{k}\) and weight \(W^{k}\) ) for generating k feature maps \(h^{k}\) with a size of ( \(m-n-1\) ) each and are convolved with input, as mentioned above. The convolution layer calculates a dot product between its input and the weights as in Eq. 1 , similar to NLP, but the inputs are undersized areas of the initial image size. Next, by applying the nonlinearity or an activation function to the convolution-layer output, we obtain the following:

The next step is down-sampling every feature map in the sub-sampling layers. This leads to a reduction in the network parameters, which accelerates the training process and in turn enables handling of the overfitting issue. For all feature maps, the pooling function (e.g. max or average) is applied to an adjacent area of size \(p \times p\) , where p is the kernel size. Finally, the FC layers receive the mid- and low-level features and create the high-level abstraction, which represents the last-stage layers as in a typical neural network. The classification scores are generated using the ending layer [e.g. support vector machines (SVMs) or softmax]. For a given instance, every score represents the probability of a specific class.

Benefits of employing CNNs

The benefits of using CNNs over other traditional neural networks in the computer vision environment are listed as follows:

The main reason to consider CNN is the weight sharing feature, which reduces the number of trainable network parameters and in turn helps the network to enhance generalization and to avoid overfitting.

Concurrently learning the feature extraction layers and the classification layer causes the model output to be both highly organized and highly reliant on the extracted features.

Large-scale network implementation is much easier with CNN than with other neural networks.

The CNN architecture consists of a number of layers (or so-called multi-building blocks). Each layer in the CNN architecture, including its function, is described in detail below.

Convolutional Layer: In CNN architecture, the most significant component is the convolutional layer. It consists of a collection of convolutional filters (so-called kernels). The input image, expressed as N-dimensional metrics, is convolved with these filters to generate the output feature map.

Kernel definition: A grid of discrete numbers or values describes the kernel. Each value is called the kernel weight. Random numbers are assigned to act as the weights of the kernel at the beginning of the CNN training process. In addition, there are several different methods used to initialize the weights. Next, these weights are adjusted at each training era; thus, the kernel learns to extract significant features.

Convolutional Operation: Initially, the CNN input format is described. The vector format is the input of the traditional neural network, while the multi-channeled image is the input of the CNN. For instance, single-channel is the format of the gray-scale image, while the RGB image format is three-channeled. To understand the convolutional operation, let us take an example of a \(4 \times 4\) gray-scale image with a \(2 \times 2\) random weight-initialized kernel. First, the kernel slides over the whole image horizontally and vertically. In addition, the dot product between the input image and the kernel is determined, where their corresponding values are multiplied and then summed up to create a single scalar value, calculated concurrently. The whole process is then repeated until no further sliding is possible. Note that the calculated dot product values represent the feature map of the output. Figure  8 graphically illustrates the primary calculations executed at each step. In this figure, the light green color represents the \(2 \times 2\) kernel, while the light blue color represents the similar size area of the input image. Both are multiplied; the end result after summing up the resulting product values (marked in a light orange color) represents an entry value to the output feature map.

figure 8

The primary calculations executed at each step of convolutional layer

However, padding to the input image is not applied in the previous example, while a stride of one (denoted for the selected step-size over all vertical or horizontal locations) is applied to the kernel. Note that it is also possible to use another stride value. In addition, a feature map of lower dimensions is obtained as a result of increasing the stride value.

On the other hand, padding is highly significant to determining border size information related to the input image. By contrast, the border side-features moves carried away very fast. By applying padding, the size of the input image will increase, and in turn, the size of the output feature map will also increase. Core Benefits of Convolutional Layers.

Sparse Connectivity: Each neuron of a layer in FC neural networks links with all neurons in the following layer. By contrast, in CNNs, only a few weights are available between two adjacent layers. Thus, the number of required weights or connections is small, while the memory required to store these weights is also small; hence, this approach is memory-effective. In addition, matrix operation is computationally much more costly than the dot (.) operation in CNN.

Weight Sharing: There are no allocated weights between any two neurons of neighboring layers in CNN, as the whole weights operate with one and all pixels of the input matrix. Learning a single group of weights for the whole input will significantly decrease the required training time and various costs, as it is not necessary to learn additional weights for each neuron.

Pooling Layer: The main task of the pooling layer is the sub-sampling of the feature maps. These maps are generated by following the convolutional operations. In other words, this approach shrinks large-size feature maps to create smaller feature maps. Concurrently, it maintains the majority of the dominant information (or features) in every step of the pooling stage. In a similar manner to the convolutional operation, both the stride and the kernel are initially size-assigned before the pooling operation is executed. Several types of pooling methods are available for utilization in various pooling layers. These methods include tree pooling, gated pooling, average pooling, min pooling, max pooling, global average pooling (GAP), and global max pooling. The most familiar and frequently utilized pooling methods are the max, min, and GAP pooling. Figure  9 illustrates these three pooling operations.

figure 9

Three types of pooling operations

Sometimes, the overall CNN performance is decreased as a result; this represents the main shortfall of the pooling layer, as this layer helps the CNN to determine whether or not a certain feature is available in the particular input image, but focuses exclusively on ascertaining the correct location of that feature. Thus, the CNN model misses the relevant information.

Activation Function (non-linearity) Mapping the input to the output is the core function of all types of activation function in all types of neural network. The input value is determined by computing the weighted summation of the neuron input along with its bias (if present). This means that the activation function makes the decision as to whether or not to fire a neuron with reference to a particular input by creating the corresponding output.

Non-linear activation layers are employed after all layers with weights (so-called learnable layers, such as FC layers and convolutional layers) in CNN architecture. This non-linear performance of the activation layers means that the mapping of input to output will be non-linear; moreover, these layers give the CNN the ability to learn extra-complicated things. The activation function must also have the ability to differentiate, which is an extremely significant feature, as it allows error back-propagation to be used to train the network. The following types of activation functions are most commonly used in CNN and other deep neural networks.

Sigmoid: The input of this activation function is real numbers, while the output is restricted to between zero and one. The sigmoid function curve is S-shaped and can be represented mathematically by Eq. 2 .

Tanh: It is similar to the sigmoid function, as its input is real numbers, but the output is restricted to between − 1 and 1. Its mathematical representation is in Eq. 3 .

ReLU: The mostly commonly used function in the CNN context. It converts the whole values of the input to positive numbers. Lower computational load is the main benefit of ReLU over the others. Its mathematical representation is in Eq. 4 .

Occasionally, a few significant issues may occur during the use of ReLU. For instance, consider an error back-propagation algorithm with a larger gradient flowing through it. Passing this gradient within the ReLU function will update the weights in a way that makes the neuron certainly not activated once more. This issue is referred to as “Dying ReLU”. Some ReLU alternatives exist to solve such issues. The following discusses some of them.

Leaky ReLU: Instead of ReLU down-scaling the negative inputs, this activation function ensures these inputs are never ignored. It is employed to solve the Dying ReLU problem. Leaky ReLU can be represented mathematically as in Eq. 5 .

Note that the leak factor is denoted by m. It is commonly set to a very small value, such as 0.001.

Noisy ReLU: This function employs a Gaussian distribution to make ReLU noisy. It can be represented mathematically as in Eq. 6 .

Parametric Linear Units: This is mostly the same as Leaky ReLU. The main difference is that the leak factor in this function is updated through the model training process. The parametric linear unit can be represented mathematically as in Eq. 7 .

Note that the learnable weight is denoted as a.

Fully Connected Layer: Commonly, this layer is located at the end of each CNN architecture. Inside this layer, each neuron is connected to all neurons of the previous layer, the so-called Fully Connected (FC) approach. It is utilized as the CNN classifier. It follows the basic method of the conventional multiple-layer perceptron neural network, as it is a type of feed-forward ANN. The input of the FC layer comes from the last pooling or convolutional layer. This input is in the form of a vector, which is created from the feature maps after flattening. The output of the FC layer represents the final CNN output, as illustrated in Fig.  10 .

figure 10

Fully connected layer

Loss Functions: The previous section has presented various layer-types of CNN architecture. In addition, the final classification is achieved from the output layer, which represents the last layer of the CNN architecture. Some loss functions are utilized in the output layer to calculate the predicted error created across the training samples in the CNN model. This error reveals the difference between the actual output and the predicted one. Next, it will be optimized through the CNN learning process.

However, two parameters are used by the loss function to calculate the error. The CNN estimated output (referred to as the prediction) is the first parameter. The actual output (referred to as the label) is the second parameter. Several types of loss function are employed in various problem types. The following concisely explains some of the loss function types.

Cross-Entropy or Softmax Loss Function: This function is commonly employed for measuring the CNN model performance. It is also referred to as the log loss function. Its output is the probability \(p \in \left\{ 0\left. , 1 \right\} \right. \) . In addition, it is usually employed as a substitution of the square error loss function in multi-class classification problems. In the output layer, it employs the softmax activations to generate the output within a probability distribution. The mathematical representation of the output class probability is Eq. 8 .

Here, \(e^{a_{i}}\) represents the non-normalized output from the preceding layer, while N represents the number of neurons in the output layer. Finally, the mathematical representation of cross-entropy loss function is Eq. 9 .

Euclidean Loss Function: This function is widely used in regression problems. In addition, it is also the so-called mean square error. The mathematical expression of the estimated Euclidean loss is Eq. 10 .

Hinge Loss Function: This function is commonly employed in problems related to binary classification. This problem relates to maximum-margin-based classification; this is mostly important for SVMs, which use the hinge loss function, wherein the optimizer attempts to maximize the margin around dual objective classes. Its mathematical formula is Eq. 11 .

The margin m is commonly set to 1. Moreover, the predicted output is denoted as \(p_{_{i}}\) , while the desired output is denoted as \(y_{_{i}}\) .

Regularization to CNN

For CNN models, over-fitting represents the central issue associated with obtaining well-behaved generalization. The model is entitled over-fitted in cases where the model executes especially well on training data and does not succeed on test data (unseen data) which is more explained in the latter section. An under-fitted model is the opposite; this case occurs when the model does not learn a sufficient amount from the training data. The model is referred to as “just-fitted” if it executes well on both training and testing data. These three types are illustrated in Fig.  11 . Various intuitive concepts are used to help the regularization to avoid over-fitting; more details about over-fitting and under-fitting are discussed in latter sections.

Dropout: This is a widely utilized technique for generalization. During each training epoch, neurons are randomly dropped. In doing this, the feature selection power is distributed equally across the whole group of neurons, as well as forcing the model to learn different independent features. During the training process, the dropped neuron will not be a part of back-propagation or forward-propagation. By contrast, the full-scale network is utilized to perform prediction during the testing process.

Drop-Weights: This method is highly similar to dropout. In each training epoch, the connections between neurons (weights) are dropped rather than dropping the neurons; this represents the only difference between drop-weights and dropout.

Data Augmentation: Training the model on a sizeable amount of data is the easiest way to avoid over-fitting. To achieve this, data augmentation is used. Several techniques are utilized to artificially expand the size of the training dataset. More details can be found in the latter section, which describes the data augmentation techniques.

Batch Normalization: This method ensures the performance of the output activations [ 81 ]. This performance follows a unit Gaussian distribution. Subtracting the mean and dividing by the standard deviation will normalize the output at each layer. While it is possible to consider this as a pre-processing task at each layer in the network, it is also possible to differentiate and to integrate it with other networks. In addition, it is employed to reduce the “internal covariance shift” of the activation layers. In each layer, the variation in the activation distribution defines the internal covariance shift. This shift becomes very high due to the continuous weight updating through training, which may occur if the samples of the training data are gathered from numerous dissimilar sources (for example, day and night images). Thus, the model will consume extra time for convergence, and in turn, the time required for training will also increase. To resolve this issue, a layer representing the operation of batch normalization is applied in the CNN architecture.

The advantages of utilizing batch normalization are as follows:

It prevents the problem of vanishing gradient from arising.

It can effectively control the poor weight initialization.

It significantly reduces the time required for network convergence (for large-scale datasets, this will be extremely useful).

It struggles to decrease training dependency across hyper-parameters.

Chances of over-fitting are reduced, since it has a minor influence on regularization.

figure 11

Over-fitting and under-fitting issues

Optimizer selection

This section discusses the CNN learning process. Two major issues are included in the learning process: the first issue is the learning algorithm selection (optimizer), while the second issue is the use of many enhancements (such as AdaDelta, Adagrad, and momentum) along with the learning algorithm to enhance the output.

Loss functions, which are founded on numerous learnable parameters (e.g. biases, weights, etc.) or minimizing the error (variation between actual and predicted output), are the core purpose of all supervised learning algorithms. The techniques of gradient-based learning for a CNN network appear as the usual selection. The network parameters should always update though all training epochs, while the network should also look for the locally optimized answer in all training epochs in order to minimize the error.

The learning rate is defined as the step size of the parameter updating. The training epoch represents a complete repetition of the parameter update that involves the complete training dataset at one time. Note that it needs to select the learning rate wisely so that it does not influence the learning process imperfectly, although it is a hyper-parameter.

Gradient Descent or Gradient-based learning algorithm: To minimize the training error, this algorithm repetitively updates the network parameters through every training epoch. More specifically, to update the parameters correctly, it needs to compute the objective function gradient (slope) by applying a first-order derivative with respect to the network parameters. Next, the parameter is updated in the reverse direction of the gradient to reduce the error. The parameter updating process is performed though network back-propagation, in which the gradient at every neuron is back-propagated to all neurons in the preceding layer. The mathematical representation of this operation is as Eq. 12 .

The final weight in the current training epoch is denoted by \(w_{i j^{t}}\) , while the weight in the preceding \((t-1)\) training epoch is denoted \(w_{i j^{t-1}}\) . The learning rate is \(\eta \) and the prediction error is E . Different alternatives of the gradient-based learning algorithm are available and commonly employed; these include the following:

Batch Gradient Descent: During the execution of this technique [ 82 ], the network parameters are updated merely one time behind considering all training datasets via the network. In more depth, it calculates the gradient of the whole training set and subsequently uses this gradient to update the parameters. For a small-sized dataset, the CNN model converges faster and creates an extra-stable gradient using BGD. Since the parameters are changed only once for every training epoch, it requires a substantial amount of resources. By contrast, for a large training dataset, additional time is required for converging, and it could converge to a local optimum (for non-convex instances).

Stochastic Gradient Descent: The parameters are updated at each training sample in this technique [ 83 ]. It is preferred to arbitrarily sample the training samples in every epoch in advance of training. For a large-sized training dataset, this technique is both more memory-effective and much faster than BGD. However, because it is frequently updated, it takes extremely noisy steps in the direction of the answer, which in turn causes the convergence behavior to become highly unstable.

Mini-batch Gradient Descent: In this approach, the training samples are partitioned into several mini-batches, in which every mini-batch can be considered an under-sized collection of samples with no overlap between them [ 84 ]. Next, parameter updating is performed following gradient computation on every mini-batch. The advantage of this method comes from combining the advantages of both BGD and SGD techniques. Thus, it has a steady convergence, more computational efficiency and extra memory effectiveness. The following describes several enhancement techniques in gradient-based learning algorithms (usually in SGD), which further powerfully enhance the CNN training process.

Momentum: For neural networks, this technique is employed in the objective function. It enhances both the accuracy and the training speed by summing the computed gradient at the preceding training step, which is weighted via a factor \(\lambda \) (known as the momentum factor). However, it therefore simply becomes stuck in a local minimum rather than a global minimum. This represents the main disadvantage of gradient-based learning algorithms. Issues of this kind frequently occur if the issue has no convex surface (or solution space).

Together with the learning algorithm, momentum is used to solve this issue, which can be expressed mathematically as in Eq. 13 .

The weight increment in the current \(t^{\prime} \text{th}\) training epoch is denoted as \( \Delta w_{i j^{t}}\) , while \(\eta \) is the learning rate, and the weight increment in the preceding \((t-1)^{\prime} \text{th}\) training epoch. The momentum factor value is maintained within the range 0 to 1; in turn, the step size of the weight updating increases in the direction of the bare minimum to minimize the error. As the value of the momentum factor becomes very low, the model loses its ability to avoid the local bare minimum. By contrast, as the momentum factor value becomes high, the model develops the ability to converge much more rapidly. If a high value of momentum factor is used together with LR, then the model could miss the global bare minimum by crossing over it.

However, when the gradient varies its direction continually throughout the training process, then the suitable value of the momentum factor (which is a hyper-parameter) causes a smoothening of the weight updating variations.

Adaptive Moment Estimation (Adam): It is another optimization technique or learning algorithm that is widely used. Adam [ 85 ] represents the latest trends in deep learning optimization. This is represented by the Hessian matrix, which employs a second-order derivative. Adam is a learning strategy that has been designed specifically for training deep neural networks. More memory efficient and less computational power are two advantages of Adam. The mechanism of Adam is to calculate adaptive LR for each parameter in the model. It integrates the pros of both Momentum and RMSprop. It utilizes the squared gradients to scale the learning rate as RMSprop and it is similar to the momentum by using the moving average of the gradient. The equation of Adam is represented in Eq. 14 .

Design of algorithms (backpropagation)

Let’s start with a notation that refers to weights in the network unambiguously. We denote \({\varvec{w}}_{i j}^{h}\) to be the weight for the connection from \(\text {ith}\) input or (neuron at \(\left. (\text {h}-1){\text{th}}\right) \) to the \(j{\text{t }}\) neuron in the \(\text {hth}\) layer. So, Fig. 12 shows the weight on a connection from the neuron in the first layer to another neuron in the next layer in the network.

figure 12

MLP structure

Where \(w_{11}^{2}\) has represented the weight from the first neuron in the first layer to the first neuron in the second layer, based on that the second weight for the same neuron will be \(w_{21}^{2}\) which means is the weight comes from the second neuron in the previous layer to the first layer in the next layer which is the second in this net. Regarding the bias, since the bias is not the connection between the neurons for the layers, so it is easily handled each neuron must have its own bias, some network each layer has a certain bias. It can be seen from the above net that each layer has its own bias. Each network has the parameters such as the no of the layer in the net, the number of the neurons in each layer, no of the weight (connection) between the layers, the no of connection can be easily determined based on the no of neurons in each layer, for example, if there are ten input fully connect with two neurons in the next layer then the number of connection between them is \((10 * 2=20\) connection, weights), how the error is defined, and the weight is updated, we will imagine there is there are two layers in our neural network,

where \(\text {d}\) is the label of induvial input \(\text {ith}\) and \(\text {y}\) is the output of the same individual input. Backpropagation is about understanding how to change the weights and biases in a network based on the changes of the cost function (Error). Ultimately, this means computing the partial derivatives \(\partial \text {E} / \partial \text {w}_{\text {ij}}^{h}\) and \(\partial \text {E} / \partial \text {b}_{\text {j}}^{h}.\) But to compute those, a local variable is introduced, \(\delta _{j}^{1}\) which is called the local error in the \(j{\text{th} }\) neuron in the \(h{\text{th} }\) layer. Based on that local error Backpropagation will give the procedure to compute \(\partial \text {E} / \partial \text {w}_{\text {ij}}^{h}\) and \(\partial \text {E} / \partial \text {b}_{\text {j}}^{h}\) how the error is defined, and the weight is updated, we will imagine there is there are two layers in our neural network that is shown in Fig. 13 .

figure 13

Neuron activation functions

Output error for \(\delta _{\text {j}}^{1}\) each \(1=1: \text {L}\) where \(\text {L}\) is no. of neuron in output

where \(\text {e}(\text {k})\) is the error of the epoch \(\text {k}\) as shown in Eq. ( 2 ) and \(\varvec{\vartheta }^{\prime }\left( {\varvec{v}}_{j}({\varvec{k}})\right) \) is the derivate of the activation function for \(v_{j}\) at the output.

Backpropagate the error at all the rest layer except the output

where \(\delta _{j}^{1}({\mathbf {k}})\) is the output error and \(w_{j l}^{h+1}(k)\) is represented the weight after the layer where the error need to obtain.

After finding the error at each neuron in each layer, now we can update the weight in each layer based on Eqs. ( 16 ) and ( 17 ).

Improving performance of CNN

Based on our experiments in different DL applications [ 86 , 87 , 88 ]. We can conclude the most active solutions that may improve the performance of CNN are:

Expand the dataset with data augmentation or use transfer learning (explained in latter sections).

Increase the training time.

Increase the depth (or width) of the model.

Add regularization.

Increase hyperparameters tuning.

CNN architectures

Over the last 10 years, several CNN architectures have been presented [ 21 , 26 ]. Model architecture is a critical factor in improving the performance of different applications. Various modifications have been achieved in CNN architecture from 1989 until today. Such modifications include structural reformulation, regularization, parameter optimizations, etc. Conversely, it should be noted that the key upgrade in CNN performance occurred largely due to the processing-unit reorganization, as well as the development of novel blocks. In particular, the most novel developments in CNN architectures were performed on the use of network depth. In this section, we review the most popular CNN architectures, beginning from the AlexNet model in 2012 and ending at the High-Resolution (HR) model in 2020. Studying these architectures features (such as input size, depth, and robustness) is the key to help researchers to choose the suitable architecture for the their target task. Table  2 presents the brief overview of CNN architectures.

The history of deep CNNs began with the appearance of LeNet [ 89 ] (Fig.  14 ). At that time, the CNNs were restricted to handwritten digit recognition tasks, which cannot be scaled to all image classes. In deep CNN architecture, AlexNet is highly respected [ 30 ], as it achieved innovative results in the fields of image recognition and classification. Krizhevesky et al. [ 30 ] first proposed AlexNet and consequently improved the CNN learning ability by increasing its depth and implementing several parameter optimization strategies. Figure  15 illustrates the basic design of the AlexNet architecture.

figure 14

The architecture of LeNet

figure 15

The architecture of AlexNet

The learning ability of the deep CNN was limited at this time due to hardware restrictions. To overcome these hardware limitations, two GPUs (NVIDIA GTX 580) were used in parallel to train AlexNet. Moreover, in order to enhance the applicability of the CNN to different image categories, the number of feature extraction stages was increased from five in LeNet to seven in AlexNet. Regardless of the fact that depth enhances generalization for several image resolutions, it was in fact overfitting that represented the main drawback related to the depth. Krizhevesky et al. used Hinton’s idea to address this problem [ 90 , 91 ]. To ensure that the features learned by the algorithm were extra robust, Krizhevesky et al.’s algorithm randomly passes over several transformational units throughout the training stage. Moreover, by reducing the vanishing gradient problem, ReLU [ 92 ] could be utilized as a non-saturating activation function to enhance the rate of convergence [ 93 ]. Local response normalization and overlapping subsampling were also performed to enhance the generalization by decreasing the overfitting. To improve on the performance of previous networks, other modifications were made by using large-size filters \((5\times 5 \; \text{and}\; 11 \times 11)\) in the earlier layers. AlexNet has considerable significance in the recent CNN generations, as well as beginning an innovative research era in CNN applications.

Network-in-network

This network model, which has some slight differences from the preceding models, introduced two innovative concepts [ 94 ]. The first was employing multiple layers of perception convolution. These convolutions are executed using a 1×1 filter, which supports the addition of extra nonlinearity in the networks. Moreover, this supports enlarging the network depth, which may later be regularized using dropout. For DL models, this idea is frequently employed in the bottleneck layer. As a substitution for a FC layer, the GAP is also employed, which represents the second novel concept and enables a significant reduction in the number of model parameters. In addition, GAP considerably updates the network architecture. Generating a final low-dimensional feature vector with no reduction in the feature maps dimension is possible when GAP is used on a large feature map [ 95 , 96 ]. Figure  16 shows the structure of the network.

figure 16

The architecture of network-in-network

Before 2013, the CNN learning mechanism was basically constructed on a trial-and-error basis, which precluded an understanding of the precise purpose following the enhancement. This issue restricted the deep CNN performance on convoluted images. In response, Zeiler and Fergus introduced DeconvNet (a multilayer de-convolutional neural network) in 2013 [ 97 ]. This method later became known as ZefNet, which was developed in order to quantitively visualize the network. Monitoring the CNN performance via understanding the neuron activation was the purpose of the network activity visualization. However, Erhan et al. utilized this exact concept to optimize deep belief network (DBN) performance by visualizing the features of the hidden layers [ 98 ]. Moreover, in addition to this issue, Le et al. assessed the deep unsupervised auto-encoder (AE) performance by visualizing the created classes of the image using the output neurons [ 99 ]. By reversing the operation order of the convolutional and pooling layers, DenconvNet operates like a forward-pass CNN. Reverse mapping of this kind launches the convolutional layer output backward to create visually observable image shapes that accordingly give the neural interpretation of the internal feature representation learned at each layer [ 100 ]. Monitoring the learning schematic through the training stage was the key concept underlying ZefNet. In addition, it utilized the outcomes to recognize an ability issue coupled with the model. This concept was experimentally proven on AlexNet by applying DeconvNet. This indicated that only certain neurons were working, while the others were out of action in the first two layers of the network. Furthermore, it indicated that the features extracted via the second layer contained aliasing objects. Thus, Zeiler and Fergus changed the CNN topology due to the existence of these outcomes. In addition, they executed parameter optimization, and also exploited the CNN learning by decreasing the stride and the filter sizes in order to retain all features of the initial two convolutional layers. An improvement in performance was accordingly achieved due to this rearrangement in CNN topology. This rearrangement proposed that the visualization of the features could be employed to identify design weaknesses and conduct appropriate parameter alteration. Figure  17 shows the structure of the network.

figure 17

The architecture of ZefNet

Visual geometry group (VGG)

After CNN was determined to be effective in the field of image recognition, an easy and efficient design principle for CNN was proposed by Simonyan and Zisserman. This innovative design was called Visual Geometry Group (VGG). A multilayer model [ 101 ], it featured nineteen more layers than ZefNet [ 97 ] and AlexNet [ 30 ] to simulate the relations of the network representational capacity in depth. Conversely, in the 2013-ILSVRC competition, ZefNet was the frontier network, which proposed that filters with small sizes could enhance the CNN performance. With reference to these results, VGG inserted a layer of the heap of \(3\times 3\) filters rather than the \(5\times 5\) and 11 × 11 filters in ZefNet. This showed experimentally that the parallel assignment of these small-size filters could produce the same influence as the large-size filters. In other words, these small-size filters made the receptive field similarly efficient to the large-size filters \((7 \times 7 \; \text{and}\; 5 \times 5)\) . By decreasing the number of parameters, an extra advantage of reducing computational complication was achieved by using small-size filters. These outcomes established a novel research trend for working with small-size filters in CNN. In addition, by inserting \(1\times 1\) convolutions in the middle of the convolutional layers, VGG regulates the network complexity. It learns a linear grouping of the subsequent feature maps. With respect to network tuning, a max pooling layer [ 102 ] is inserted following the convolutional layer, while padding is implemented to maintain the spatial resolution. In general, VGG obtained significant results for localization problems and image classification. While it did not achieve first place in the 2014-ILSVRC competition, it acquired a reputation due to its enlarged depth, homogenous topology, and simplicity. However, VGG’s computational cost was excessive due to its utilization of around 140 million parameters, which represented its main shortcoming. Figure  18 shows the structure of the network.

figure 18

The architecture of VGG

In the 2014-ILSVRC competition, GoogleNet (also called Inception-V1) emerged as the winner [ 103 ]. Achieving high-level accuracy with decreased computational cost is the core aim of the GoogleNet architecture. It proposed a novel inception block (module) concept in the CNN context, since it combines multiple-scale convolutional transformations by employing merge, transform, and split functions for feature extraction. Figure  19 illustrates the inception block architecture. This architecture incorporates filters of different sizes ( \(5\times 5, 3\times 3, \; \text{and} \; 1\times 1\) ) to capture channel information together with spatial information at diverse ranges of spatial resolution. The common convolutional layer of GoogLeNet is substituted by small blocks using the same concept of network-in-network (NIN) architecture [ 94 ], which replaced each layer with a micro-neural network. The GoogLeNet concepts of merge, transform, and split were utilized, supported by attending to an issue correlated with different learning types of variants existing in a similar class of several images. The motivation of GoogLeNet was to improve the efficiency of CNN parameters, as well as to enhance the learning capacity. In addition, it regulates the computation by inserting a \(1\times 1\) convolutional filter, as a bottleneck layer, ahead of using large-size kernels. GoogleNet employed sparse connections to overcome the redundant information problem. It decreased cost by neglecting the irrelevant channels. It should be noted here that only some of the input channels are connected to some of the output channels. By employing a GAP layer as the end layer, rather than utilizing a FC layer, the density of connections was decreased. The number of parameters was also significantly decreased from 40 to 5 million parameters due to these parameter tunings. The additional regularity factors used included the employment of RmsProp as optimizer and batch normalization [ 104 ]. Furthermore, GoogleNet proposed the idea of auxiliary learners to speed up the rate of convergence. Conversely, the main shortcoming of GoogleNet was its heterogeneous topology; this shortcoming requires adaptation from one module to another. Other shortcomings of GoogleNet include the representation jam, which substantially decreased the feature space in the following layer, and in turn occasionally leads to valuable information loss.

figure 19

The basic structure of Google Block

Highway network

Increasing the network depth enhances its performance, mainly for complicated tasks. By contrast, the network training becomes difficult. The presence of several layers in deeper networks may result in small gradient values of the back-propagation of error at lower layers. In 2015, Srivastava et al. [ 105 ] suggested a novel CNN architecture, called Highway Network, to overcome this issue. This approach is based on the cross-connectivity concept. The unhindered information flow in Highway Network is empowered by instructing two gating units inside the layer. The gate mechanism concept was motivated by LSTM-based RNN [ 106 , 107 ]. The information aggregation was conducted by merging the information of the \(\i{\text{th}}-k\) layers with the next \(\i{\text{th}}\) layer to generate a regularization impact, which makes the gradient-based training of the deeper network very simple. This empowers the training of networks with more than 100 layers, such as a deeper network of 900 layers with the SGD algorithm. A Highway Network with a depth of fifty layers presented an improved rate of convergence, which is better than thin and deep architectures at the same time [ 108 ]. By contrast, [ 69 ] empirically demonstrated that plain Net performance declines when more than ten hidden layers are inserted. It should be noted that even a Highway Network 900 layers in depth converges much more rapidly than the plain network.

He et al. [ 37 ] developed ResNet (Residual Network), which was the winner of ILSVRC 2015. Their objective was to design an ultra-deep network free of the vanishing gradient issue, as compared to the previous networks. Several types of ResNet were developed based on the number of layers (starting with 34 layers and going up to 1202 layers). The most common type was ResNet50, which comprised 49 convolutional layers plus a single FC layer. The overall number of network weights was 25.5 M, while the overall number of MACs was 3.9 M. The novel idea of ResNet is its use of the bypass pathway concept, as shown in Fig.  20 , which was employed in Highway Nets to address the problem of training a deeper network in 2015. This is illustrated in Fig.  20 , which contains the fundamental ResNet block diagram. This is a conventional feedforward network plus a residual connection. The residual layer output can be identified as the \((l - 1){\text{th}}\) outputs, which are delivered from the preceding layer \((x_{l} - 1)\) . After executing different operations [such as convolution using variable-size filters, or batch normalization, before applying an activation function like ReLU on \((x_{l} - 1)\) ], the output is \(F(x_{l} - 1)\) . The ending residual output is \(x_{l}\) , which can be mathematically represented as in Eq. 18 .

There are numerous basic residual blocks included in the residual network. Based on the type of the residual network architecture, operations in the residual block are also changed [ 37 ].

figure 20

The block diagram for ResNet

In comparison to the highway network, ResNet presented shortcut connections inside layers to enable cross-layer connectivity, which are parameter-free and data-independent. Note that the layers characterize non-residual functions when a gated shortcut is closed in the highway network. By contrast, the individuality shortcuts are never closed, while the residual information is permanently passed in ResNet. Furthermore, ResNet has the potential to prevent the problems of gradient diminishing, as the shortcut connections (residual links) accelerate the deep network convergence. ResNet was the winner of the 2015-ILSVRC championship with 152 layers of depth; this represents 8 times the depth of VGG and 20 times the depth of AlexNet. In comparison with VGG, it has lower computational complexity, even with enlarged depth.

Inception: ResNet and Inception-V3/4

Szegedy et al. [ 103 , 109 , 110 ] proposed Inception-ResNet and Inception-V3/4 as upgraded types of Inception-V1/2. The concept behind Inception-V3 was to minimize the computational cost with no effect on the deeper network generalization. Thus, Szegedy et al. used asymmetric small-size filters ( \(1\times 5\) and \(1\times 7\) ) rather than large-size filters ( \( 7\times 7\) and \(5\times 5\) ); moreover, they utilized a bottleneck of \(1\times 1\) convolution prior to the large-size filters [ 110 ]. These changes make the operation of the traditional convolution very similar to cross-channel correlation. Previously, Lin et al. utilized the 1 × 1 filter potential in NIN architecture [ 94 ]. Subsequently, [ 110 ] utilized the same idea in an intelligent manner. By using \(1\times 1\) convolutional operation in Inception-V3, the input data are mapped into three or four isolated spaces, which are smaller than the initial input spaces. Next, all of these correlations are mapped in these smaller spaces through common \(5\times 5\) or \(3\times 3\) convolutions. By contrast, in Inception-ResNet, Szegedy et al. bring together the inception block and the residual learning power by replacing the filter concatenation with the residual connection [ 111 ]. Szegedy et al. empirically demonstrated that Inception-ResNet (Inception-4 with residual connections) can achieve a similar generalization power to Inception-V4 with enlarged width and depth and without residual connections. Thus, it is clearly illustrated that using residual connections in training will significantly accelerate the Inception network training. Figure  21 shows The basic block diagram for Inception Residual unit.

figure 21

The basic block diagram for Inception Residual unit

To solve the problem of the vanishing gradient, DenseNet was presented, following the same direction as ResNet and the Highway network [ 105 , 111 , 112 ]. One of the drawbacks of ResNet is that it clearly conserves information by means of preservative individuality transformations, as several layers contribute extremely little or no information. In addition, ResNet has a large number of weights, since each layer has an isolated group of weights. DenseNet employed cross-layer connectivity in an improved approach to address this problem [ 112 , 113 , 114 ]. It connected each layer to all layers in the network using a feed-forward approach. Therefore, the feature maps of each previous layer were employed to input into all of the following layers. In traditional CNNs, there are l connections between the previous layer and the current layer, while in DenseNet, there are \(\frac{l(l+1)}{2}\) direct connections. DenseNet demonstrates the influence of cross-layer depth wise-convolutions. Thus, the network gains the ability to discriminate clearly between the added and the preserved information, since DenseNet concatenates the features of the preceding layers rather than adding them. However, due to its narrow layer structure, DenseNet becomes parametrically high-priced in addition to the increased number of feature maps. The direct admission of all layers to the gradients via the loss function enhances the information flow all across the network. In addition, this includes a regularizing impact, which minimizes overfitting on tasks alongside minor training sets. Figure  22 shows the architecture of DenseNet Network.

figure 22

(adopted from [ 112 ])

The architecture of DenseNet Network

ResNext is an enhanced version of the Inception Network [ 115 ]. It is also known as the Aggregated Residual Transform Network. Cardinality, which is a new term presented by [ 115 ], utilized the split, transform, and merge topology in an easy and effective way. It denotes the size of the transformation set as an extra dimension [ 116 , 117 , 118 ]. However, the Inception network manages network resources more efficiently, as well as enhancing the learning ability of the conventional CNN. In the transformation branch, different spatial embeddings (employing e.g. \(5\times 5\) , \(3\times 3\) , and \(1\times 1\) ) are used. Thus, customizing each layer is required separately. By contrast, ResNext derives its characteristic features from ResNet, VGG, and Inception. It employed the VGG deep homogenous topology with the basic architecture of GoogleNet by setting \(3\times 3\) filters as spatial resolution inside the blocks of split, transform, and merge. Figure  23 shows the ResNext building blocks. ResNext utilized multi-transformations inside the blocks of split, transform, and merge, as well as outlining such transformations in cardinality terms. The performance is significantly improved by increasing the cardinality, as Xie et al. showed. The complexity of ResNext was regulated by employing \(1\times 1\) filters (low embeddings) ahead of a \(3\times 3\) convolution. By contrast, skipping connections are used for optimized training [ 115 ].

figure 23

The basic block diagram for the ResNext building blocks

The feature reuse problem is the core shortcoming related to deep residual networks, since certain feature blocks or transformations contribute a very small amount to learning. Zagoruyko and Komodakis [ 119 ] accordingly proposed WideResNet to address this problem. These authors advised that the depth has a supplemental influence, while the residual units convey the core learning ability of deep residual networks. WideResNet utilized the residual block power via making the ResNet wider instead of deeper [ 37 ]. It enlarged the width by presenting an extra factor, k, which handles the network width. In other words, it indicated that layer widening is a highly successful method of performance enhancement compared to deepening the residual network. While enhanced representational capacity is achieved by deep residual networks, these networks also have certain drawbacks, such as the exploding and vanishing gradient problems, feature reuse problem (inactivation of several feature maps), and the time-intensive nature of the training. He et al. [ 37 ] tackled the feature reuse problem by including a dropout in each residual block to regularize the network in an efficient manner. In a similar manner, utilizing dropouts, Huang et al. [ 120 ] presented the stochastic depth concept to solve the slow learning and gradient vanishing problems. Earlier research was focused on increasing the depth; thus, any small enhancement in performance required the addition of several new layers. When comparing the number of parameters, WideResNet has twice that of ResNet, as an experimental study showed. By contrast, WideResNet presents an improved method for training relative to deep networks [ 119 ]. Note that most architectures prior to residual networks (including the highly effective VGG and Inception) were wider than ResNet. Thus, wider residual networks were established once this was determined. However, inserting a dropout between the convolutional layers (as opposed to within the residual block) made the learning more effective in WideResNet [ 121 , 122 ].

Pyramidal Net

The depth of the feature map increases in the succeeding layer due to the deep stacking of multi-convolutional layers, as shown in previous deep CNN architectures such as ResNet, VGG, and AlexNet. By contrast, the spatial dimension reduces, since a sub-sampling follows each convolutional layer. Thus, augmented feature representation is recompensed by decreasing the size of the feature map. The extreme expansion in the depth of the feature map, alongside the spatial information loss, interferes with the learning ability in the deep CNNs. ResNet obtained notable outcomes for the issue of image classification. Conversely, deleting a convolutional block—in which both the number of channel and spatial dimensions vary (channel depth enlarges, while spatial dimension reduces)—commonly results in decreased classifier performance. Accordingly, the stochastic ResNet enhanced the performance by decreasing the information loss accompanying the residual unit drop. Han et al. [ 123 ] proposed Pyramidal Net to address the ResNet learning interference problem. To address the depth enlargement and extreme reduction in spatial width via ResNet, Pyramidal Net slowly enlarges the residual unit width to cover the most feasible places rather than saving the same spatial dimension inside all residual blocks up to the appearance of the down-sampling. It was referred to as Pyramidal Net due to the slow enlargement in the feature map depth based on the up-down method. Factor l, which was determined by Eq. 19 , regulates the depth of the feature map.

Here, the dimension of the l th residual unit is indicated by \(d_{l}\) ; moreover, n indicates the overall number of residual units, the step factor is indicated by \(\lambda \) , and the depth increase is regulated by the factor \(\frac{\lambda }{n}\) , which uniformly distributes the weight increase across the dimension of the feature map. Zero-padded identity mapping is used to insert the residual connections among the layers. In comparison to the projection-based shortcut connections, zero-padded identity mapping requires fewer parameters, which in turn leads to enhanced generalization [ 124 ]. Multiplication- and addition-based widening are two different approaches used in Pyramidal Nets for network widening. More specifically, the first approach (multiplication) enlarges geometrically, while the second one (addition) enlarges linearly [ 92 ]. The main problem associated with the width enlargement is the growth in time and space required related to the quadratic time.

Extreme inception architecture is the main characteristic of Xception. The main idea behind Xception is its depthwise separable convolution [ 125 ]. The Xception model adjusted the original inception block by making it wider and exchanging a single dimension ( \(3 \times 3\) ) followed by a \(1 \times 1\) convolution to reduce computational complexity. Figure  24 shows the Xception block architecture. The Xception network becomes extra computationally effective through the use of the decoupling channel and spatial correspondence. Moreover, it first performs mapping of the convolved output to the embedding short dimension by applying \(1 \times 1\) convolutions. It then performs k spatial transformations. Note that k here represents the width-defining cardinality, which is obtained via the transformations number in Xception. However, the computations were made simpler in Xception by distinctly convolving each channel around the spatial axes. These axes are subsequently used as the \(1 \times 1\) convolutions (pointwise convolution) for performing cross-channel correspondence. The \(1 \times 1\) convolution is utilized in Xception to regularize the depth of the channel. The traditional convolutional operation in Xception utilizes a number of transformation segments equivalent to the number of channels; Inception, moreover, utilizes three transformation segments, while traditional CNN architecture utilizes only a single transformation segment. Conversely, the suggested Xception transformation approach achieves extra learning efficiency and better performance but does not minimize the number of parameters [ 126 , 127 ].

figure 24

The basic block diagram for the Xception block architecture

Residual attention neural network

To improve the network feature representation, Wang et al. [ 128 ] proposed the Residual Attention Network (RAN). Enabling the network to learn aware features of the object is the main purpose of incorporating attention into the CNN. The RAN consists of stacked residual blocks in addition to the attention module; hence, it is a feed-forward CNN. However, the attention module is divided into two branches, namely the mask branch and trunk branch. These branches adopt a top-down and bottom-up learning strategy respectively. Encapsulating two different strategies in the attention model supports top-down attention feedback and fast feed-forward processing in only one particular feed-forward process. More specifically, the top-down architecture generates dense features to make inferences about every aspect. Moreover, the bottom-up feedforward architecture generates low-resolution feature maps in addition to robust semantic information. Restricted Boltzmann machines employed a top-down bottom-up strategy as in previously proposed studies [ 129 ]. During the training reconstruction phase, Goh et al. [ 130 ] used the mechanism of top-down attention in deep Boltzmann machines (DBMs) as a regularizing factor. Note that the network can be globally optimized using a top-down learning strategy in a similar manner, where the maps progressively output to the input throughout the learning process [ 129 , 130 , 131 , 132 ].

Incorporating the attention concept with convolutional blocks in an easy way was used by the transformation network, as obtained in a previous study [ 133 ]. Unfortunately, these are inflexible, which represents the main problem, along with their inability to be used for varying surroundings. By contrast, stacking multi-attention modules has made RAN very effective at recognizing noisy, complex, and cluttered images. RAN’s hierarchical organization gives it the capability to adaptively allocate a weight for every feature map depending on its importance within the layers. Furthermore, incorporating three distinct levels of attention (spatial, channel, and mixed) enables the model to use this ability to capture the object-aware features at these distinct levels.

Convolutional block attention module

The importance of the feature map utilization and the attention mechanism is certified via SE-Network and RAN [ 128 , 134 , 135 ]. The convolutional block attention (CBAM) module, which is a novel attention-based CNN, was first developed by Woo et al. [ 136 ]. This module is similar to SE-Network and simple in design. SE-Network disregards the object’s spatial locality in the image and considers only the channels’ contribution during the image classification. Regarding object detection, object spatial location plays a significant role. The convolutional block attention module sequentially infers the attention maps. More specifically, it applies channel attention preceding the spatial attention to obtain the refined feature maps. Spatial attention is performed using 1 × 1 convolution and pooling functions, as in the literature. Generating an effective feature descriptor can be achieved by using a spatial axis along with the pooling of features. In addition, generating a robust spatial attention map is possible, as CBAM concatenates the max pooling and average pooling operations. In a similar manner, a collection of GAP and max pooling operations is used to model the feature map statistics. Woo et al. [ 136 ] demonstrated that utilizing GAP will return a sub-optimized inference of channel attention, whereas max pooling provides an indication of the distinguishing object features. Thus, the utilization of max pooling and average pooling enhances the network’s representational power. The feature maps improve the representational power, as well as facilitating a focus on the significant portion of the chosen features. The expression of 3D attention maps through a serial learning procedure assists in decreasing the computational cost and the number of parameters, as Woo et al. [ 136 ] experimentally proved. Note that any CNN architecture can be simply integrated with CBAM.

Concurrent spatial and channel excitation mechanism

To make the work valid for segmentation tasks, Roy et al. [ 137 , 138 ] expanded Hu et al. [ 134 ] effort by adding the influence of spatial information to the channel information. Roy et al. [ 137 , 138 ] presented three types of modules: (1) channel squeeze and excitation with concurrent channels (scSE); (2) exciting spatially and squeezing channel-wise (sSE); (3) exciting channel-wise and squeezing spatially (cSE). For segmentation purposes, they employed auto-encoder-based CNNs. In addition, they suggested inserting modules following the encoder and decoder layers. To specifically highlight the object-specific feature maps, they further allocated attention to every channel by expressing a scaling factor from the channel and spatial information in the first module (scSE). In the second module (sSE), the feature map information has lower importance than the spatial locality, as the spatial information plays a significant role during the segmentation process. Therefore, several channel collections are spatially divided and developed so that they can be employed in segmentation. In the final module (cSE), a similar SE-block concept is used. Furthermore, the scaling factor is derived founded on the contribution of the feature maps within the object detection [ 137 , 138 ].

CNN is an efficient technique for detecting object features and achieving well-behaved recognition performance in comparison with innovative handcrafted feature detectors. A number of restrictions related to CNN are present, meaning that the CNN does not consider certain relations, orientation, size, and perspectives of features. For instance, when considering a face image, the CNN does not count the various face components (such as mouth, eyes, nose, etc.) positions, and will incorrectly activate the CNN neurons and recognize the face without taking specific relations (such as size, orientation etc.) into account. At this point, consider a neuron that has probability in addition to feature properties such as size, orientation, perspective, etc. A specific neuron/capsule of this type has the ability to effectively detect the face along with different types of information. Thus, many layers of capsule nodes are used to construct the capsule network. An encoding unit, which contains three layers of capsule nodes, forms the CapsuleNet or CapsNet (the initial version of the capsule networks).

For example, the MNIST architecture comprises \(28\times 28\) images, applying 256 filters of size \(9\times 9\) and with stride 1. The \(28-9+1=20\) is the output plus 256 feature maps. Next, these outputs are input to the first capsule layer, while producing an 8D vector rather than a scalar; in fact, this is a modified convolution layer. Note that a stride 2 with \(9\times 9\) filters is employed in the first convolution layer. Thus, the dimension of the output is \((20-9)/2+1=6\) . The initial capsules employ \(8\times 32\) filters, which generate 32 × 8 × 6 × 6 (32 for groups, 8 for neurons, while 6 × 6 is the neuron size).

Figure  25 represents the complete CapsNet encoding and decoding processes. In the CNN context, a max-pooling layer is frequently employed to handle the translation change. It can detect the feature moves in the event that the feature is still within the max-pooling window. This approach has the ability to detect the overlapped features; this is highly significant in detection and segmentation operations, since the capsule involves the weighted features sum from the preceding layer.

figure 25

The complete CapsNet encoding and decoding processes

In conventional CNNs, a particular cost function is employed to evaluate the global error that grows toward the back throughout the training process. Conversely, in such cases, the activation of a neuron will not grow further once the weight between two neurons turns out to be zero. Instead of a single size being provided with the complete cost function in repetitive dynamic routing alongside the agreement, the signal is directed based on the feature parameters. Sabour et al. [ 139 ] provides more details about this architecture. When using MNIST to recognize handwritten digits, this innovative CNN architecture gives superior accuracy. From the application perspective, this architecture has extra suitability for segmentation and detection approaches when compared with classification approaches [ 140 , 141 , 142 ].

High-resolution network (HRNet)

High-resolution representations are necessary for position-sensitive vision tasks, such as semantic segmentation, object detection, and human pose estimation. In the present up-to-date frameworks, the input image is encoded as a low-resolution representation using a subnetwork that is constructed as a connected series of high-to-low resolution convolutions such as VGGNet and ResNet. The low-resolution representation is then recovered to become a high-resolution one. Alternatively, high-resolution representations are maintained during the entire process using a novel network, referred to as a High-Resolution Network (HRNet) [ 143 , 144 ]. This network has two principal features. First, the convolution series of high-to-low resolutions are connected in parallel. Second, the information across the resolutions are repeatedly exchanged. The advantage achieved includes getting a representation that is more accurate in the spatial domain and extra-rich in the semantic domain. Moreover, HRNet has several applications in the fields of object detection, semantic segmentation, and human pose prediction. For computer vision problems, the HRNet represents a more robust backbone. Figure  26 illustrates the general architecture of HRNet.

figure 26

The general architecture of HRNet

Challenges (limitations) of deep learning and alternate solutions

When employing DL, several difficulties are often taken into consideration. Those more challenging are listed next and several possible alternatives are accordingly provided.

Training data

DL is extremely data-hungry considering it also involves representation learning [ 145 , 146 ]. DL demands an extensively large amount of data to achieve a well-behaved performance model, i.e. as the data increases, an extra well-behaved performance model can be achieved (Fig.  27 ). In most cases, the available data are sufficient to obtain a good performance model. However, sometimes there is a shortage of data for using DL directly [ 87 ]. To properly address this issue, three suggested methods are available. The first involves the employment of the transfer-learning concept after data is collected from similar tasks. Note that while the transferred data will not directly augment the actual data, it will help in terms of both enhancing the original input representation of data and its mapping function [ 147 ]. In this way, the model performance is boosted. Another technique involves employing a well-trained model from a similar task and fine-tuning the ending of two layers or even one layer based on the limited original data. Refer to [ 148 , 149 ] for a review of different transfer-learning techniques applied in the DL approach. In the second method, data augmentation is performed [ 150 ]. This task is very helpful for use in augmenting the image data, since the image translation, mirroring, and rotation commonly do not change the image label. Conversely, it is important to take care when applying this technique in some cases such as with bioinformatics data. For instance, when mirroring an enzyme sequence, the output data may not represent the actual enzyme sequence. In the third method, the simulated data can be considered for increasing the volume of the training set. It is occasionally possible to create simulators based on the physical process if the issue is well understood. Therefore, the result will involve the simulation of as much data as needed. Processing the data requirement for DL-based simulation is obtained as an example in Ref. [ 151 ].

figure 27

The performance of DL regarding the amount of data

  • Transfer learning

Recent research has revealed a widespread use of deep CNNs, which offer ground-breaking support for answering many classification problems. Generally speaking, deep CNN models require a sizable volume of data to obtain good performance. The common challenge associated with using such models concerns the lack of training data. Indeed, gathering a large volume of data is an exhausting job, and no successful solution is available at this time. The undersized dataset problem is therefore currently solved using the TL technique [ 148 , 149 ], which is highly efficient in addressing the lack of training data issue. The mechanism of TL involves training the CNN model with large volumes of data. In the next step, the model is fine-tuned for training on a small request dataset.

The student-teacher relationship is a suitable approach to clarifying TL. Gathering detailed knowledge of the subject is the first step [ 152 ]. Next, the teacher provides a “course” by conveying the information within a “lecture series” over time. Put simply, the teacher transfers the information to the student. In more detail, the expert (teacher) transfers the knowledge (information) to the learner (student). Similarly, the DL network is trained using a vast volume of data, and also learns the bias and the weights during the training process. These weights are then transferred to different networks for retraining or testing a similar novel model. Thus, the novel model is enabled to pre-train weights rather than requiring training from scratch. Figure  28 illustrates the conceptual diagram of the TL technique.

Pre-trained models: Many CNN models, e.g. AlexNet [ 30 ], GoogleNet [ 103 ], and ResNet [ 37 ], have been trained on large datasets such as ImageNet for image recognition purposes. These models can then be employed to recognize a different task without the need to train from scratch. Furthermore, the weights remain the same apart from a few learned features. In cases where data samples are lacking, these models are very useful. There are many reasons for employing a pre-trained model. First, training large models on sizeable datasets requires high-priced computational power. Second, training large models can be time-consuming, taking up to multiple weeks. Finally, a pre-trained model can assist with network generalization and speed up the convergence.

A research problem using pre-trained models: Training a DL approach requires a massive number of images. Thus, obtaining good performance is a challenge under these circumstances. Achieving excellent outcomes in image classification or recognition applications, with performance occasionally superior to that of a human, becomes possible through the use of deep convolutional neural networks (DCNNs) including several layers if a huge amount of data is available [ 37 , 148 , 153 ]. However, avoiding overfitting problems in such applications requires sizable datasets and properly generalizing DCNN models. When training a DCNN model, the dataset size has no lower limit. However, the accuracy of the model becomes insufficient in the case of the utilized model has fewer layers, or if a small dataset is used for training due to over- or under-fitting problems. Due to they have no ability to utilize the hierarchical features of sizable datasets, models with fewer layers have poor accuracy. It is difficult to acquire sufficient training data for DL models. For example, in medical imaging and environmental science, gathering labelled datasets is very costly [ 148 ]. Moreover, the majority of the crowdsourcing workers are unable to make accurate notes on medical or biological images due to their lack of medical or biological knowledge. Thus, ML researchers often rely on field experts to label such images; however, this process is costly and time consuming. Therefore, producing the large volume of labels required to develop flourishing deep networks turns out to be unfeasible. Recently, TL has been widely employed to address the later issue. Nevertheless, although TL enhances the accuracy of several tasks in the fields of pattern recognition and computer vision [ 154 , 155 ], there is an essential issue related to the source data type used by the TL as compared to the target dataset. For instance, enhancing the medical image classification performance of CNN models is achieved by training the models using the ImageNet dataset, which contains natural images [ 153 ]. However, such natural images are completely dissimilar from the raw medical images, meaning that the model performance is not enhanced. It has further been proven that TL from different domains does not significantly affect performance on medical imaging tasks, as lightweight models trained from scratch perform nearly as well as standard ImageNet-transferred models [ 156 ]. Therefore, there exists scenarios in which using pre-trained models do not become an affordable solution. In 2020, some researchers have utilized same-domain TL and achieved excellent results [ 86 , 87 , 88 , 157 ]. Same-domain TL is an approach of using images that look similar to the target dataset for training. For example, using X-ray images of different chest diseases to train the model, then fine-tuning and training it on chest X-ray images for COVID-19 diagnosis. More details about same-domain TL and how to implement the fine-tuning process can be found in [ 87 ].

figure 28

The conceptual diagram of the TL technique

Data augmentation techniques

If the goal is to increase the amount of available data and avoid the overfitting issue, data augmentation techniques are one possible solution [ 150 , 158 , 159 ]. These techniques are data-space solutions for any limited-data problem. Data augmentation incorporates a collection of methods that improve the attributes and size of training datasets. Thus, DL networks can perform better when these techniques are employed. Next, we list some data augmentation alternate solutions.

Flipping: Flipping the vertical axis is a less common practice than flipping the horizontal one. Flipping has been verified as valuable on datasets like ImageNet and CIFAR-10. Moreover, it is highly simple to implement. In addition, it is not a label-conserving transformation on datasets that involve text recognition (such as SVHN and MNIST).

Color space: Encoding digital image data is commonly used as a dimension tensor ( \(height \times width \times color channels\) ). Accomplishing augmentations in the color space of the channels is an alternative technique, which is extremely workable for implementation. A very easy color augmentation involves separating a channel of a particular color, such as Red, Green, or Blue. A simple way to rapidly convert an image using a single-color channel is achieved by separating that matrix and inserting additional double zeros from the remaining two color channels. Furthermore, increasing or decreasing the image brightness is achieved by using straightforward matrix operations to easily manipulate the RGB values. By deriving a color histogram that describes the image, additional improved color augmentations can be obtained. Lighting alterations are also made possible by adjusting the intensity values in histograms similar to those employed in photo-editing applications.

Cropping: Cropping a dominant patch of every single image is a technique employed with combined dimensions of height and width as a specific processing step for image data. Furthermore, random cropping may be employed to produce an impact similar to translations. The difference between translations and random cropping is that translations conserve the spatial dimensions of this image, while random cropping reduces the input size [for example from (256, 256) to (224, 224)]. According to the selected reduction threshold for cropping, the label-preserving transformation may not be addressed.

Rotation: When rotating an image left or right from within 0 to 360 degrees around the axis, rotation augmentations are obtained. The rotation degree parameter greatly determines the suitability of the rotation augmentations. In digit recognition tasks, small rotations (from 0 to 20 degrees) are very helpful. By contrast, the data label cannot be preserved post-transformation when the rotation degree increases.

Translation: To avoid positional bias within the image data, a very useful transformation is to shift the image up, down, left, or right. For instance, it is common that the whole dataset images are centered; moreover, the tested dataset should be entirely made up of centered images to test the model. Note that when translating the initial images in a particular direction, the residual space should be filled with Gaussian or random noise, or a constant value such as 255 s or 0 s. The spatial dimensions of the image post-augmentation are preserved using this padding.

Noise injection This approach involves injecting a matrix of arbitrary values. Such a matrix is commonly obtained from a Gaussian distribution. Moreno-Barea et al. [ 160 ] employed nine datasets to test the noise injection. These datasets were taken from the UCI repository [ 161 ]. Injecting noise within images enables the CNN to learn additional robust features.

However, highly well-behaved solutions for positional biases available within the training data are achieved by means of geometric transformations. To separate the distribution of the testing data from the training data, several prospective sources of bias exist. For instance, when all faces should be completely centered within the frames (as in facial recognition datasets), the problem of positional biases emerges. Thus, geometric translations are the best solution. Geometric translations are helpful due to their simplicity of implementation, as well as their effective capability to disable the positional biases. Several libraries of image processing are available, which enables beginning with simple operations such as rotation or horizontal flipping. Additional training time, higher computational costs, and additional memory are some shortcomings of geometric transformations. Furthermore, a number of geometric transformations (such as arbitrary cropping or translation) should be manually observed to ensure that they do not change the image label. Finally, the biases that separate the test data from the training data are more complicated than transitional and positional changes. Hence, it is not trivial answering to when and where geometric transformations are suitable to be applied.

Imbalanced data

Commonly, biological data tend to be imbalanced, as negative samples are much more numerous than positive ones [ 162 , 163 , 164 ]. For example, compared to COVID-19-positive X-ray images, the volume of normal X-ray images is very large. It should be noted that undesirable results may be produced when training a DL model using imbalanced data. The following techniques are used to solve this issue. First, it is necessary to employ the correct criteria for evaluating the loss, as well as the prediction result. In considering the imbalanced data, the model should perform well on small classes as well as larger ones. Thus, the model should employ area under curve (AUC) as the resultant loss as well as the criteria [ 165 ]. Second, it should employ the weighted cross-entropy loss, which ensures the model will perform well with small classes if it still prefers to employ the cross-entropy loss. Simultaneously, during model training, it is possible either to down-sample the large classes or up-sample the small classes. Finally, to make the data balanced as in Ref. [ 166 ], it is possible to construct models for every hierarchical level, as a biological system frequently has hierarchical label space. However, the effect of the imbalanced data on the performance of the DL model has been comprehensively investigated. In addition, to lessen the problem, the most frequently used techniques were also compared. Nevertheless, note that these techniques are not specified for biological problems.

Interpretability of data

Occasionally, DL techniques are analyzed to act as a black box. In fact, they are interpretable. The need for a method of interpreting DL, which is used to obtain the valuable motifs and patterns recognized by the network, is common in many fields, such as bioinformatics [ 167 ]. In the task of disease diagnosis, it is not only required to know the disease diagnosis or prediction results of a trained DL model, but also how to enhance the surety of the prediction outcomes, as the model makes its decisions based on these verifications [ 168 ]. To achieve this, it is possible to give a score of importance for every portion of the particular example. Within this solution, back-propagation-based techniques or perturbation-based approaches are used [ 169 ]. In the perturbation-based approaches, a portion of the input is changed and the effect of this change on the model output is observed [ 170 , 171 , 172 , 173 ]. This concept has high computational complexity, but it is simple to understand. On the other hand, to check the score of the importance of various input portions, the signal from the output propagates back to the input layer in the back-propagation-based techniques. These techniques have been proven valuable in [ 174 ]. In different scenarios, various meanings can represent the model interpretability.

Uncertainty scaling

Commonly, the final prediction label is not the only label required when employing DL techniques to achieve the prediction; the score of confidence for every inquiry from the model is also desired. The score of confidence is defined as how confident the model is in its prediction [ 175 ]. Since the score of confidence prevents belief in unreliable and misleading predictions, it is a significant attribute, regardless of the application scenario. In biology, the confidence score reduces the resources and time expended in proving the outcomes of the misleading prediction. Generally speaking, in healthcare or similar applications, the uncertainty scaling is frequently very significant; it helps in evaluating automated clinical decisions and the reliability of machine learning-based disease-diagnosis [ 176 , 177 ]. Because overconfident prediction can be the output of different DL models, the score of probability (achieved from the softmax output of the direct-DL) is often not in the correct scale [ 178 ]. Note that the softmax output requires post-scaling to achieve a reliable probability score. For outputting the probability score in the correct scale, several techniques have been introduced, including Bayesian Binning into Quantiles (BBQ) [ 179 ], isotonic regression [ 180 ], histogram binning [ 181 ], and the legendary Platt scaling [ 182 ]. More specifically, for DL techniques, temperature scaling was recently introduced, which achieves superior performance compared to the other techniques.

Catastrophic forgetting

This is defined as incorporating new information into a plain DL model, made possible by interfering with the learned information. For instance, consider a case where there are 1000 types of flowers and a model is trained to classify these flowers, after which a new type of flower is introduced; if the model is fine-tuned only with this new class, its performance will become unsuccessful with the older classes [ 183 , 184 ]. The logical data are continually collected and renewed, which is in fact a highly typical scenario in many fields, e.g. Biology. To address this issue, there is a direct solution that involves employing old and new data to train an entirely new model from scratch. This solution is time-consuming and computationally intensive; furthermore, it leads to an unstable state for the learned representation of the initial data. At this time, three different types of ML techniques, which have not catastrophic forgetting, are made available to solve the human brain problem founded on the neurophysiological theories [ 185 , 186 ]. Techniques of the first type are founded on regularizations such as EWC [ 183 ] Techniques of the second type employ rehearsal training techniques and dynamic neural network architecture like iCaRL [ 187 , 188 ]. Finally, techniques of the third type are founded on dual-memory learning systems [ 189 ]. Refer to [ 190 , 191 , 192 ] in order to gain more details.

Model compression

To obtain well-trained models that can still be employed productively, DL models have intensive memory and computational requirements due to their huge complexity and large numbers of parameters [ 193 , 194 ]. One of the fields that is characterized as data-intensive is the field of healthcare and environmental science. These needs reduce the deployment of DL in limited computational-power machines, mainly in the healthcare field. The numerous methods of assessing human health and the data heterogeneity have become far more complicated and vastly larger in size [ 195 ]; thus, the issue requires additional computation [ 196 ]. Furthermore, novel hardware-based parallel processing solutions such as FPGAs and GPUs [ 197 , 198 , 199 ] have been developed to solve the computation issues associated with DL. Recently, numerous techniques for compressing the DL models, designed to decrease the computational issues of the models from the starting point, have also been introduced. These techniques can be classified into four classes. In the first class, the redundant parameters (which have no significant impact on model performance) are reduced. This class, which includes the famous deep compression method, is called parameter pruning [ 200 ]. In the second class, the larger model uses its distilled knowledge to train a more compact model; thus, it is called knowledge distillation [ 201 , 202 ]. In the third class, compact convolution filters are used to reduce the number of parameters [ 203 ]. In the final class, the information parameters are estimated for preservation using low-rank factorization [ 204 ]. For model compression, these classes represent the most representative techniques. In [ 193 ], it has been provided a more comprehensive discussion about the topic.

Overfitting

DL models have excessively high possibilities of resulting in data overfitting at the training stage due to the vast number of parameters involved, which are correlated in a complex manner. Such situations reduce the model’s ability to achieve good performance on the tested data [ 90 , 205 ]. This problem is not only limited to a specific field, but involves different tasks. Therefore, when proposing DL techniques, this problem should be fully considered and accurately handled. In DL, the implied bias of the training process enables the model to overcome crucial overfitting problems, as recent studies suggest [ 205 , 206 , 207 , 208 ]. Even so, it is still necessary to develop techniques that handle the overfitting problem. An investigation of the available DL algorithms that ease the overfitting problem can categorize them into three classes. The first class acts on both the model architecture and model parameters and includes the most familiar approaches, such as weight decay [ 209 ], batch normalization [ 210 ], and dropout [ 90 ]. In DL, the default technique is weight decay [ 209 ], which is used extensively in almost all ML algorithms as a universal regularizer. The second class works on model inputs such as data corruption and data augmentation [ 150 , 211 ]. One reason for the overfitting problem is the lack of training data, which makes the learned distribution not mirror the real distribution. Data augmentation enlarges the training data. By contrast, marginalized data corruption improves the solution exclusive to augmenting the data. The final class works on the model output. A recently proposed technique penalizes the over-confident outputs for regularizing the model [ 178 ]. This technique has demonstrated the ability to regularize RNNs and CNNs.

Vanishing gradient problem

In general, when using backpropagation and gradient-based learning techniques along with ANNs, largely in the training stage, a problem called the vanishing gradient problem arises [ 212 , 213 , 214 ]. More specifically, in each training iteration, every weight of the neural network is updated based on the current weight and is proportionally relative to the partial derivative of the error function. However, this weight updating may not occur in some cases due to a vanishingly small gradient, which in the worst case means that no extra training is possible and the neural network will stop completely. Conversely, similarly to other activation functions, the sigmoid function shrinks a large input space to a tiny input space. Thus, the derivative of the sigmoid function will be small due to large variation at the input that produces a small variation at the output. In a shallow network, only some layers use these activations, which is not a significant issue. While using more layers will lead the gradient to become very small in the training stage, in this case, the network works efficiently. The back-propagation technique is used to determine the gradients of the neural networks. Initially, this technique determines the network derivatives of each layer in the reverse direction, starting from the last layer and progressing back to the first layer. The next step involves multiplying the derivatives of each layer down the network in a similar manner to the first step. For instance, multiplying N small derivatives together when there are N hidden layers employs an activation function such as the sigmoid function. Hence, the gradient declines exponentially while propagating back to the first layer. More specifically, the biases and weights of the first layers cannot be updated efficiently during the training stage because the gradient is small. Moreover, this condition decreases the overall network accuracy, as these first layers are frequently critical to recognizing the essential elements of the input data. However, such a problem can be avoided through employing activation functions. These functions lack the squishing property, i.e., the ability to squish the input space to within a small space. By mapping X to max, the ReLU [ 91 ] is the most popular selection, as it does not yield a small derivative that is employed in the field. Another solution involves employing the batch normalization layer [ 81 ]. As mentioned earlier, the problem occurs once a large input space is squashed into a small space, leading to vanishing the derivative. Employing batch normalization degrades this issue by simply normalizing the input, i.e., the expression | x | does not accomplish the exterior boundaries of the sigmoid function. The normalization process makes the largest part of it come down in the green area, which ensures that the derivative is large enough for further actions. Furthermore, faster hardware can tackle the previous issue, e.g. that provided by GPUs. This makes standard back-propagation possible for many deeper layers of the network compared to the time required to recognize the vanishing gradient problem [ 215 ].

Exploding gradient problem

Opposite to the vanishing problem is the one related to gradient. Specifically, large error gradients are accumulated during back-propagation [ 216 , 217 , 218 ]. The latter will lead to extremely significant updates to the weights of the network, meaning that the system becomes unsteady. Thus, the model will lose its ability to learn effectively. Grosso modo, moving backward in the network during back-propagation, the gradient grows exponentially by repetitively multiplying gradients. The weight values could thus become incredibly large and may overflow to become a not-a-number (NaN) value. Some potential solutions include:

Using different weight regularization techniques.

Redesigning the architecture of the network model.

Underspecification

In 2020, a team of computer scientists at Google has identified a new challenge called underspecification [ 219 ]. ML models including DL models often show surprisingly poor behavior when they are tested in real-world applications such as computer vision, medical imaging, natural language processing, and medical genomics. The reason behind the weak performance is due to underspecification. It has been shown that small modifications can force a model towards a completely different solution as well as lead to different predictions in deployment domains. There are different techniques of addressing underspecification issue. One of them is to design “stress tests” to examine how good a model works on real-world data and to find out the possible issues. Nevertheless, this demands a reliable understanding of the process the model can work inaccurately. The team stated that “Designing stress tests that are well-matched to applied requirements, and that provide good “coverage” of potential failure modes is a major challenge”. Underspecification puts major constraints on the credibility of ML predictions and may require some reconsidering over certain applications. Since ML is linked to human by serving several applications such as medical imaging and self-driving cars, it will require proper attention to this issue.

Applications of deep learning

Presently, various DL applications are widespread around the world. These applications include healthcare, social network analysis, audio and speech processing (like recognition and enhancement), visual data processing methods (such as multimedia data analysis and computer vision), and NLP (translation and sentence classification), among others (Fig.  29 ) [ 220 , 221 , 222 , 223 , 224 ]. These applications have been classified into five categories: classification, localization, detection, segmentation, and registration. Although each of these tasks has its own target, there is fundamental overlap in the pipeline implementation of these applications as shown in Fig.  30 . Classification is a concept that categorizes a set of data into classes. Detection is used to locate interesting objects in an image with consideration given to the background. In detection, multiple objects, which could be from dissimilar classes, are surrounded by bounding boxes. Localization is the concept used to locate the object, which is surrounded by a single bounding box. In segmentation (semantic segmentation), the target object edges are surrounded by outlines, which also label them; moreover, fitting a single image (which could be 2D or 3D) onto another refers to registration. One of the most important and wide-ranging DL applications are in healthcare [ 225 , 226 , 227 , 228 , 229 , 230 ]. This area of research is critical due to its relation to human lives. Moreover, DL has shown tremendous performance in healthcare. Therefore, we take DL applications in the medical image analysis field as an example to describe the DL applications.

figure 29

Examples of DL applications

figure 30

Workflow of deep learning tasks

Classification

Computer-Aided Diagnosis (CADx) is another title sometimes used for classification. Bharati et al. [ 231 ] used a chest X-ray dataset for detecting lung diseases based on a CNN. Another study attempted to read X-ray images by employing CNN [ 232 ]. In this modality, the comparative accessibility of these images has likely enhanced the progress of DL. [ 233 ] used an improved pre-trained GoogLeNet CNN containing more than 150,000 images for training and testing processes. This dataset was augmented from 1850 chest X-rays. The creators reorganized the image orientation into lateral and frontal views and achieved approximately 100% accuracy. This work of orientation classification has clinically limited use. As a part of an ultimately fully automated diagnosis workflow, it obtained the data augmentation and pre-trained efficiency in learning the metadata of relevant images. Chest infection, commonly referred to as pneumonia, is extremely treatable, as it is a commonly occurring health problem worldwide. Conversely, Rajpurkar et al. [ 234 ] utilized CheXNet, which is an improved version of DenseNet [ 112 ] with 121 convolution layers, for classifying fourteen types of disease. These authors used the CheXNet14 dataset [ 235 ], which comprises 112,000 images. This network achieved an excellent performance in recognizing fourteen different diseases. In particular, pneumonia classification accomplished a 0.7632 AUC score using receiver operating characteristics (ROC) analysis. In addition, the network obtained better than or equal to the performance of both a three-radiologist panel and four individual radiologists. Zuo et al. [ 236 ] have adopted CNN for candidate classification in lung nodule. Shen et al. [ 237 ] employed both Random Forest (RF) and SVM classifiers with CNNs to classify lung nodules. They employed two convolutional layers with each of the three parallel CNNs. The LIDC-IDRI (Lung Image Database Consortium) dataset, which contained 1010-labeled CT lung scans, was used to classify the two types of lung nodules (malignant and benign). Different scales of the image patches were used by every CNN to extract features, while the output feature vector was constructed using the learned features. Next, these vectors were classified into malignant or benign using either the RF classifier or SVM with radial basis function (RBF) filter. The model was robust to various noisy input levels and achieved an accuracy of 86% in nodule classification. Conversely, the model of [ 238 ] interpolates the image data missing between PET and MRI images using 3D CNNs. The Alzheimer Disease Neuroimaging Initiative (ADNI) database, containing 830 PET and MRI patient scans, was utilized in their work. The PET and MRI images are used to train the 3D CNNs, first as input and then as output. Furthermore, for patients who have no PET images, the 3D CNNs utilized the trained images to rebuild the PET images. These rebuilt images approximately fitted the actual disease recognition outcomes. However, this approach did not address the overfitting issues, which in turn restricted their technique in terms of its possible capacity for generalization. Diagnosing normal versus Alzheimer’s disease patients has been achieved by several CNN models [ 239 , 240 ]. Hosseini-Asl et al. [ 241 ] attained 99% accuracy for up-to-date outcomes in diagnosing normal versus Alzheimer’s disease patients. These authors applied an auto-encoder architecture using 3D CNNs. The generic brain features were pre-trained on the CADDementia dataset. Subsequently, the outcomes of these learned features became inputs to higher layers to differentiate between patient scans of Alzheimer’s disease, mild cognitive impairment, or normal brains based on the ADNI dataset and using fine-tuned deep supervision techniques. The architectures of VGGNet and RNNs, in that order, were the basis of both VOXCNN and ResNet models developed by Korolev et al. [ 242 ]. They also discriminated between Alzheimer’s disease and normal patients using the ADNI database. Accuracy was 79% for Voxnet and 80% for ResNet. Compared to Hosseini-Asl’s work, both models achieved lower accuracies. Conversely, the implementation of the algorithms was simpler and did not require feature hand-crafting, as Korolev declared. In 2020, Mehmood et al. [ 240 ] trained a developed CNN-based network called “SCNN” with MRI images for the tasks of classification of Alzheimer’s disease. They achieved state-of-the-art results by obtaining an accuracy of 99.05%.

Recently, CNN has taken some medical imaging classification tasks to different level from traditional diagnosis to automated diagnosis with tremendous performance. Examples of these tasks are diabetic foot ulcer (DFU) (as normal and abnormal (DFU) classes) [ 87 , 243 , 244 , 245 , 246 ], sickle cells anemia (SCA) (as normal, abnormal (SCA), and other blood components) [ 86 , 247 ], breast cancer by classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue [ 42 , 88 , 248 , 249 , 250 , 251 , 252 ], and multi-class skin cancer classification [ 253 , 254 , 255 ].

In 2020, CNNs are playing a vital role in early diagnosis of the novel coronavirus (COVID-2019). CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images [ 256 , 257 , 258 , 259 , 260 ]. More details about the classification of medical imaging applications can be found in [ 226 , 261 , 262 , 263 , 264 , 265 ].

Localization

Although applications in anatomy education could increase, the practicing clinician is more likely to be interested in the localization of normal anatomy. Radiological images are independently examined and described outside of human intervention, while localization could be applied in completely automatic end-to-end applications [ 266 , 267 , 268 ]. Zhao et al. [ 269 ] introduced a new deep learning-based approach to localize pancreatic tumor in projection X-ray images for image-guided radiation therapy without the need for fiducials. Roth et al. [ 270 ] constructed and trained a CNN using five convolutional layers to classify around 4000 transverse-axial CT images. These authors used five categories for classification: legs, pelvis, liver, lung, and neck. After data augmentation techniques were applied, they achieved an AUC score of 0.998 and the classification error rate of the model was 5.9%. For detecting the positions of the spleen, kidney, heart, and liver, Shin et al. [ 271 ] employed stacked auto-encoders on 78 contrast-improved MRI scans of the stomach area containing the kidneys or liver. Temporal and spatial domains were used to learn the hierarchal features. Based on the organs, these approaches achieved detection accuracies of 62–79%. Sirazitdinov et al. [ 268 ] presented an aggregate of two convolutional neural networks, namely RetinaNet and Mask R-CNN for pneumonia detection and localization.

Computer-Aided Detection (CADe) is another method used for detection. For both the clinician and the patient, overlooking a lesion on a scan may have dire consequences. Thus, detection is a field of study requiring both accuracy and sensitivity [ 272 , 273 , 274 ]. Chouhan et al. [ 275 ] introduced an innovative deep learning framework for the detection of pneumonia by adopting the idea of transfer learning. Their approach obtained an accuracy of 96.4% with a recall of 99.62% on unseen data. In the area of COVID-19 and pulmonary disease, several convolutional neural network approaches have been proposed for automatic detection from X-ray images which showed an excellent performance [ 46 , 276 , 277 , 278 , 279 ].

In the area of skin cancer, there several applications were introduced for the detection task [ 280 , 281 , 282 ]. Thurnhofer-Hemsi et al. [ 283 ] introduced a deep learning approach for skin cancer detection by fine-tuning five state-of-art convolutional neural network models. They addressed the issue of a lack of training data by adopting the ideas of transfer learning and data augmentation techniques. DenseNet201 network has shown superior results compared to other models.

Another interesting area is that of histopathological images, which are progressively digitized. Several papers have been published in this field [ 284 , 285 , 286 , 287 , 288 , 289 , 290 ]. Human pathologists read these images laboriously; they search for malignancy markers, such as a high index of cell proliferation, using molecular markers (e.g. Ki-67), cellular necrosis signs, abnormal cellular architecture, enlarged numbers of mitotic figures denoting augmented cell replication, and enlarged nucleus-to-cytoplasm ratios. Note that the histopathological slide may contain a huge number of cells (up to the thousands). Thus, the risk of disregarding abnormal neoplastic regions is high when wading through these cells at excessive levels of magnification. Ciresan et al. [ 291 ] employed CNNs of 11–13 layers for identifying mitotic figures. Fifty breast histology images from the MITOS dataset were used. Their technique attained recall and precision scores of 0.7 and 0.88 respectively. Sirinukunwattana et al. [ 292 ] utilized 100 histology images of colorectal adenocarcinoma to detect cell nuclei using CNNs. Roughly 30,000 nuclei were hand-labeled for training purposes. The novelty of this approach was in the use of Spatially Constrained CNN. This CNN detects the center of nuclei using the surrounding spatial context and spatial regression. Instead of this CNN, Xu et al. [ 293 ] employed a stacked sparse auto-encoder (SSAE) to identify nuclei in histological slides of breast cancer, achieving 0.83 and 0.89 recall and precision scores respectively. In this field, they showed that unsupervised learning techniques are also effectively utilized. In medical images, Albarquoni et al. [ 294 ] investigated the problem of insufficient labeling. They crowd-sourced the actual mitoses labeling in the histology images of breast cancer (from amateurs online). Solving the recurrent issue of inadequate labeling during the analysis of medical images can be achieved by feeding the crowd-sourced input labels into the CNN. This method signifies a remarkable proof-of-concept effort. In 2020, Lei et al. [ 285 ] introduced the employment of deep convolutional neural networks for automatic identification of mitotic candidates from histological sections for mitosis screening. They obtained the state-of-the-art detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition.

Segmentation

Although MRI and CT image segmentation research includes different organs such as knee cartilage, prostate, and liver, most research work has concentrated on brain segmentation, particularly tumors [ 295 , 296 , 297 , 298 , 299 , 300 ]. This issue is highly significant in surgical preparation to obtain the precise tumor limits for the shortest surgical resection. During surgery, excessive sacrificing of key brain regions may lead to neurological shortfalls including cognitive damage, emotionlessness, and limb difficulty. Conventionally, medical anatomical segmentation was done by hand; more specifically, the clinician draws out lines within the complete stack of the CT or MRI volume slice by slice. Thus, it is perfect for implementing a solution that computerizes this painstaking work. Wadhwa et al. [ 301 ] presented a brief overview on brain tumor segmentation of MRI images. Akkus et al. [ 302 ] wrote a brilliant review of brain MRI segmentation that addressed the different metrics and CNN architectures employed. Moreover, they explain several competitions in detail, as well as their datasets, which included Ischemic Stroke Lesion Segmentation (ISLES), Mild Traumatic brain injury Outcome Prediction (MTOP), and Brain Tumor Segmentation (BRATS).

Chen et al. [ 299 ] proposed convolutional neural networks for precise brain tumor segmentation. The approach that they employed involves several approaches for better features learning including the DeepMedic model, a novel dual-force training scheme, a label distribution-based loss function, and Multi-Layer Perceptron-based post-processing. They conducted their method on the two most modern brain tumor segmentation datasets, i.e., BRATS 2017 and BRATS 2015 datasets. Hu et al. [ 300 ] introduced the brain tumor segmentation method by adopting a multi-cascaded convolutional neural network (MCCNN) and fully connected conditional random fields (CRFs). The achieved results were excellent compared with the state-of-the-art methods.

Moeskops et al. [ 303 ] employed three parallel-running CNNs, each of which had a 2D input patch of dissimilar size, for segmenting and classifying MRI brain images. These images, which include 35 adults and 22 pre-term infants, were classified into various tissue categories such as cerebrospinal fluid, grey matter, and white matter. Every patch concentrates on capturing various image aspects with the benefit of employing three dissimilar sizes of input patch; here, the bigger sizes incorporated the spatial features, while the lowest patch sizes concentrated on the local textures. In general, the algorithm has Dice coefficients in the range of 0.82–0.87 and achieved a satisfactory accuracy. Although 2D image slices are employed in the majority of segmentation research, Milletrate et al. [ 304 ] implemented 3D CNN for segmenting MRI prostate images. Furthermore, they used the PROMISE2012 challenge dataset, from which fifty MRI scans were used for training and thirty for testing. The U-Net architecture of Ronnerberger et al. [ 305 ] inspired their V-net. This model attained a 0.869 Dice coefficient score, the same as the winning teams in the competition. To reduce overfitting and create the model of a deeper 11-convolutional layer CNN, Pereira et al. [ 306 ] applied intentionally small-sized filters of 3x3. Their model used MRI scans of 274 gliomas (a type of brain tumor) for training. They achieved first place in the 2013 BRATS challenge, as well as second place in the BRATS challenge 2015. Havaei et al. [ 307 ] also considered gliomas using the 2013 BRATS dataset. They investigated different 2D CNN architectures. Compared to the winner of BRATS 2013, their algorithm worked better, as it required only 3 min to execute rather than 100 min. The concept of cascaded architecture formed the basis of their model. Thus, it is referred to as an InputCascadeCNN. Employing FC Conditional Random Fields (CRFs), atrous spatial pyramid pooling, and up-sampled filters were techniques introduced by Chen et al. [ 308 ]. These authors aimed to enhance the accuracy of localization and enlarge the field of view of every filter at a multi-scale. Their model, DeepLab, attained 79.7% mIOU (mean Intersection Over Union). In the PASCAL VOC-2012 image segmentation, their model obtained an excellent performance.

Recently, the Automatic segmentation of COVID-19 Lung Infection from CT Images helps to detect the development of COVID-19 infection by employing several deep learning techniques [ 309 , 310 , 311 , 312 ].

Registration

Usually, given two input images, the four main stages of the canonical procedure of the image registration task are [ 313 , 314 ]:

Target Selection: it illustrates the determined input image that the second counterpart input image needs to remain accurately superimposed to.

Feature Extraction: it computes the set of features extracted from each input image.

Feature Matching: it allows finding similarities between the previously obtained features.

Pose Optimization: it is aimed to minimize the distance between both input images.

Then, the result of the registration procedure is the suitable geometric transformation (e.g. translation, rotation, scaling, etc.) that provides both input images within the same coordinate system in a way the distance between them is minimal, i.e. their level of superimposition/overlapping is optimal. It is out of the scope of this work to provide an extensive review of this topic. Nevertheless, a short summary is accordingly introduced next.

Commonly, the input images for the DL-based registration approach could be in various forms, e.g. point clouds, voxel grids, and meshes. Additionally, some techniques allow as inputs the result of the Feature Extraction or Matching steps in the canonical scheme. Specifically, the outcome could be some data in a particular form as well as the result of the steps from the classical pipeline (feature vector, matching vector, and transformation). Nevertheless, with the newest DL-based methods, a novel conceptual type of ecosystem issues. It contains acquired characteristics about the target, materials, and their behavior that can be registered with the input data. Such a conceptual ecosystem is formed by a neural network and its training manner, and it could be counted as an input to the registration approach. Nevertheless, it is not an input that one might adopt in every registration situation since it corresponds to an interior data representation.

From a DL view-point, the interpretation of the conceptual design enables differentiating the input data of a registration approach into defined or non-defined models. In particular, the illustrated phases are models that depict particular spatial data (e.g. 2D or 3D) while a non-defined one is a generalization of a data set created by a learning system. Yumer et al. [ 315 ] developed a framework in which the model acquires characteristics of objects, meaning ready to identify what a more sporty car seems like or a more comfy chair is, also adjusting a 3D model to fit those characteristics while maintaining the main characteristics of the primary data. Likewise, a fundamental perspective of the unsupervised learning method introduced by Ding et al. [ 316 ] is that there is no target for the registration approach. In this instance, the network is able of placing each input point cloud in a global space, solving SLAM issues in which many point clouds have to be registered rigidly. On the other hand, Mahadevan [ 317 ] proposed the combination of two conceptual models utilizing the growth of Imagination Machines to give flexible artificial intelligence systems and relationships between the learned phases through training schemes that are not inspired on labels and classifications. Another practical application of DL, especially CNNs, to image registration is the 3D reconstruction of objects. Wang et al. [ 318 ] applied an adversarial way using CNNs to rebuild a 3D model of an object from its 2D image. The network learns many objects and orally accomplishes the registration between the image and the conceptual model. Similarly, Hermoza et al. [ 319 ] also utilize the GAN network for prognosticating the absent geometry of damaged archaeological objects, providing the reconstructed object based on a voxel grid format and a label selecting its class.

DL for medical image registration has numerous applications, which were listed by some review papers [ 320 , 321 , 322 ]. Yang et al. [ 323 ] implemented stacked convolutional layers as an encoder-decoder approach to predict the morphing of the input pixel into its last formation using MRI brain scans from the OASIS dataset. They employed a registration model known as Large Deformation Diffeomorphic Metric Mapping (LDDMM) and attained remarkable enhancements in computation time. Miao et al. [ 324 ] used synthetic X-ray images to train a five-layer CNN to register 3D models of a trans-esophageal probe, a hand implant, and a knee implant onto 2D X-ray images for pose estimation. They determined that their model achieved an execution time of 0.1 s, representing an important enhancement against the conventional registration techniques based on intensity; moreover, it achieved effective registrations 79–99% of the time. Li et al. [ 325 ] introduced a neural network-based approach for the non-rigid 2D–3D registration of the lateral cephalogram and the volumetric cone-beam CT (CBCT) images.

Computational approaches

For computationally exhaustive applications, complex ML and DL approaches have rapidly been identified as the most significant techniques and are widely used in different fields. The development and enhancement of algorithms aggregated with capabilities of well-behaved computational performance and large datasets make it possible to effectively execute several applications, as earlier applications were either not possible or difficult to take into consideration.

Currently, several standard DNN configurations are available. The interconnection patterns between layers and the total number of layers represent the main differences between these configurations. The Table  2 illustrates the growth rate of the overall number of layers over time, which seems to be far faster than the “Moore’s Law growth rate”. In normal DNN, the number of layers grew by around 2.3× each year in the period from 2012 to 2016. Recent investigations of future ResNet versions reveal that the number of layers can be extended up to 1000. However, an SGD technique is employed to fit the weights (or parameters), while different optimization techniques are employed to obtain parameter updating during the DNN training process. Repetitive updates are required to enhance network accuracy in addition to a minorly augmented rate of enhancement. For example, the training process using ImageNet as a large dataset, which contains more than 14 million images, along with ResNet as a network model, take around 30K to 40K repetitions to converge to a steady solution. In addition, the overall computational load, as an upper-level prediction, may exceed 1020 FLOPS when both the training set size and the DNN complexity increase.

Prior to 2008, boosting the training to a satisfactory extent was achieved by using GPUs. Usually, days or weeks are needed for a training session, even with GPU support. By contrast, several optimization strategies were developed to reduce the extensive learning time. The computational requirements are believed to increase as the DNNs continuously enlarge in both complexity and size.

In addition to the computational load cost, the memory bandwidth and capacity have a significant effect on the entire training performance, and to a lesser extent, deduction. More specifically, the parameters are distributed through every layer of the input data, there is a sizeable amount of reused data, and the computation of several network layers exhibits an excessive computation-to-bandwidth ratio. By contrast, there are no distributed parameters, the amount of reused data is extremely small, and the additional FC layers have an extremely small computation-to-bandwidth ratio. Table  3 presents a comparison between different aspects related to the devices. In addition, the table is established to facilitate familiarity with the tradeoffs by obtaining the optimal approach for configuring a system based on either FPGA, GPU, or CPU devices. It should be noted that each has corresponding weaknesses and strengths; accordingly, there are no clear one-size-fits-all solutions.

Although GPU processing has enhanced the ability to address the computational challenges related to such networks, the maximum GPU (or CPU) performance is not achieved, and several techniques or models have turned out to be strongly linked to bandwidth. In the worst cases, the GPU efficiency is between 15 and 20% of the maximum theoretical performance. This issue is required to enlarge the memory bandwidth using high-bandwidth stacked memory. Next, different approaches based on FPGA, GPU, and CPU are accordingly detailed.

CPU-based approach

The well-behaved performance of the CPU nodes usually assists robust network connectivity, storage abilities, and large memory. Although CPU nodes are more common-purpose than those of FPGA or GPU, they lack the ability to match them in unprocessed computation facilities, since this requires increased network ability and a larger memory capacity.

GPU-based approach

GPUs are extremely effective for several basic DL primitives, which include greatly parallel-computing operations such as activation functions, matrix multiplication, and convolutions [ 326 , 327 , 328 , 329 , 330 ]. Incorporating HBM-stacked memory into the up-to-date GPU models significantly enhances the bandwidth. This enhancement allows numerous primitives to efficiently utilize all computational resources of the available GPUs. The improvement in GPU performance over CPU performance is usually 10-20:1 related to dense linear algebra operations.

Maximizing parallel processing is the base of the initial GPU programming model. For example, a GPU model may involve up to sixty-four computational units. There are four SIMD engines per each computational layer, and each SIMD has sixteen floating-point computation lanes. The peak performance is 25 TFLOPS (fp16) and 10 TFLOPS (fp32) as the percentage of the employment approaches 100%. Additional GPU performance may be achieved if the addition and multiply functions for vectors combine the inner production instructions for matching primitives related to matrix operations.

For DNN training, the GPU is usually considered to be an optimized design, while for inference operations, it may also offer considerable performance improvements.

FPGA-based approach

FPGA is wildly utilized in various tasks including deep learning [ 199 , 247 , 331 , 332 , 333 , 334 ]. Inference accelerators are commonly implemented utilizing FPGA. The FPGA can be effectively configured to reduce the unnecessary or overhead functions involved in GPU systems. Compared to GPU, the FPGA is restricted to both weak-behaved floating-point performance and integer inference. The main FPGA aspect is the capability to dynamically reconfigure the array characteristics (at run-time), as well as the capability to configure the array by means of effective design with little or no overhead.

As mentioned earlier, the FPGA offers both performance and latency for every watt it gains over GPU and CPU in DL inference operations. Implementation of custom high-performance hardware, pruned networks, and reduced arithmetic precision are three factors that enable the FPGA to implement DL algorithms and to achieve FPGA with this level of efficiency. In addition, FPGA may be employed to implement CNN overlay engines with over 80% efficiency, eight-bit accuracy, and over 15 TOPs peak performance; this is used for a few conventional CNNs, as Xillinx and partners demonstrated recently. By contrast, pruning techniques are mostly employed in the LSTM context. The sizes of the models can be efficiently minimized by up to 20×, which provides an important benefit during the implementation of the optimal solution, as MLP neural processing demonstrated. A recent study in the field of implementing fixed-point precision and custom floating-point has revealed that lowering the 8-bit is extremely promising; moreover, it aids in supplying additional advancements to implementing peak performance FPGA related to the DNN models.

Evaluation metrics

Evaluation metrics adopted within DL tasks play a crucial role in achieving the optimized classifier [ 335 ]. They are utilized within a usual data classification procedure through two main stages: training and testing. It is utilized to optimize the classification algorithm during the training stage. This means that the evaluation metric is utilized to discriminate and select the optimized solution, e.g., as a discriminator, which can generate an extra-accurate forecast of upcoming evaluations related to a specific classifier. For the time being, the evaluation metric is utilized to measure the efficiency of the created classifier, e.g. as an evaluator, within the model testing stage using hidden data. As given in Eq. 20 , TN and TP are defined as the number of negative and positive instances, respectively, which are successfully classified. In addition, FN and FP are defined as the number of misclassified positive and negative instances respectively. Next, some of the most well-known evaluation metrics are listed below.

Accuracy: Calculates the ratio of correct predicted classes to the total number of samples evaluated (Eq. 20 ).

Sensitivity or Recall: Utilized to calculate the fraction of positive patterns that are correctly classified (Eq. 21 ).

Specificity: Utilized to calculate the fraction of negative patterns that are correctly classified (Eq. 22 ).

Precision: Utilized to calculate the positive patterns that are correctly predicted by all predicted patterns in a positive class (Eq. 23 ).

F1-Score: Calculates the harmonic average between recall and precision rates (Eq. 24 ).

J Score: This metric is also called Youdens J statistic. Eq. 25 represents the metric.

False Positive Rate (FPR): This metric refers to the possibility of a false alarm ratio as calculated in Eq. 26

Area Under the ROC Curve: AUC is a common ranking type metric. It is utilized to conduct comparisons between learning algorithms [ 336 , 337 , 338 ], as well as to construct an optimal learning model [ 339 , 340 ]. In contrast to probability and threshold metrics, the AUC value exposes the entire classifier ranking performance. The following formula is used to calculate the AUC value for two-class problem [ 341 ] (Eq. 27 )

Here, \(S_{p}\) represents the sum of all positive ranked samples. The number of negative and positive samples is denoted as \(n_{n}\) and \(n_{p}\) , respectively. Compared to the accuracy metrics, the AUC value was verified empirically and theoretically, making it very helpful for identifying an optimized solution and evaluating the classifier performance through classification training.

When considering the discrimination and evaluation processes, the AUC performance was brilliant. However, for multiclass issues, the AUC computation is primarily cost-effective when discriminating a large number of created solutions. In addition, the time complexity for computing the AUC is \(O \left( |C|^{2} \; n\log n\right) \) with respect to the Hand and Till AUC model [ 341 ] and \(O \left( |C| \; n\log n\right) \) according to Provost and Domingo’s AUC model [ 336 ].

Frameworks and datasets

Several DL frameworks and datasets have been developed in the last few years. various frameworks and libraries have also been used in order to expedite the work with good results. Through their use, the training process has become easier. Table  4 lists the most utilized frameworks and libraries.

Based on the star ratings on Github, as well as our own background in the field, TensorFlow is deemed the most effective and easy to use. It has the ability to work on several platforms. (Github is one of the biggest software hosting sites, while Github stars refer to how well-regarded a project is on the site). Moreover, there are several other benchmark datasets employed for different DL tasks. Some of these are listed in Table  5 .

Summary and conclusion

Finally, it is mandatory the inclusion of a brief discussion by gathering all the relevant data provided along this extensive research. Next, an itemized analysis is presented in order to conclude our review and exhibit the future directions.

DL already experiences difficulties in simultaneously modeling multi-complex modalities of data. In recent DL developments, another common approach is that of multimodal DL.

DL requires sizeable datasets (labeled data preferred) to predict unseen data and to train the models. This challenge turns out to be particularly difficult when real-time data processing is required or when the provided datasets are limited (such as in the case of healthcare data). To alleviate this issue, TL and data augmentation have been researched over the last few years.

Although ML slowly transitions to semi-supervised and unsupervised learning to manage practical data without the need for manual human labeling, many of the current deep-learning models utilize supervised learning.

The CNN performance is greatly influenced by hyper-parameter selection. Any small change in the hyper-parameter values will affect the general CNN performance. Therefore, careful parameter selection is an extremely significant issue that should be considered during optimization scheme development.

Impressive and robust hardware resources like GPUs are required for effective CNN training. Moreover, they are also required for exploring the efficiency of using CNN in smart and embedded systems.

In the CNN context, ensemble learning [ 342 , 343 ] represents a prospective research area. The collection of different and multiple architectures will support the model in improving its generalizability across different image categories through extracting several levels of semantic image representation. Similarly, ideas such as new activation functions, dropout, and batch normalization also merit further investigation.

The exploitation of depth and different structural adaptations is significantly improved in the CNN learning capacity. Substituting the traditional layer configuration with blocks results in significant advances in CNN performance, as has been shown in the recent literature. Currently, developing novel and efficient block architectures is the main trend in new research models of CNN architectures. HRNet is only one example that shows there are always ways to improve the architecture.

It is expected that cloud-based platforms will play an essential role in the future development of computational DL applications. Utilizing cloud computing offers a solution to handling the enormous amount of data. It also helps to increase efficiency and reduce costs. Furthermore, it offers the flexibility to train DL architectures.

With the recent development in computational tools including a chip for neural networks and a mobile GPU, we will see more DL applications on mobile devices. It will be easier for users to use DL.

Regarding the issue of lack of training data, It is expected that various techniques of transfer learning will be considered such as training the DL model on large unlabeled image datasets and next transferring the knowledge to train the DL model on a small number of labeled images for the same task.

Last, this overview provides a starting point for the community of DL being interested in the field of DL. Furthermore, researchers would be allowed to decide the more suitable direction of work to be taken in order to provide more accurate alternatives to the field.

Availability of data and materials

Not applicable.

Rozenwald MB, Galitsyna AA, Sapunov GV, Khrameeva EE, Gelfand MS. A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features. PeerJ Comput Sci. 2020;6:307.

Article   Google Scholar  

Amrit C, Paauw T, Aly R, Lavric M. Identifying child abuse through text mining and machine learning. Expert Syst Appl. 2017;88:402–18.

Hossain E, Khan I, Un-Noor F, Sikander SS, Sunny MSH. Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access. 2019;7:13960–88.

Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Al Najada H. Survey of review spam detection using machine learning techniques. J Big Data. 2015;2(1):23.

Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, Quadrana M. Content-based video recommendation system based on stylistic visual features. J Data Semant. 2016;5(2):99–113.

Al-Dulaimi K, Chandran V, Nguyen K, Banks J, Tomeo-Reyes I. Benchmarking hep-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape. Pattern Recogn Lett. 2019;125:534–41.

Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017;234:11–26.

Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar S. A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR). 2018;51(5):1–36.

Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK. A state-of-the-art survey on deep learning theory and architectures. Electronics. 2019;8(3):292.

Potok TE, Schuman C, Young S, Patton R, Spedalieri F, Liu J, Yao KT, Rose G, Chakma G. A study of complex deep learning networks on high-performance, neuromorphic, and quantum computers. ACM J Emerg Technol Comput Syst (JETC). 2018;14(2):1–21.

Adeel A, Gogate M, Hussain A. Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments. Inf Fusion. 2020;59:163–70.

Tian H, Chen SC, Shyu ML. Evolutionary programming based deep learning feature selection and network construction for visual data classification. Inf Syst Front. 2020;22(5):1053–66.

Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag. 2018;13(3):55–75.

Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology. 2021;46(1):176–90.

Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol. 1. IEEE; 2005. p. 886–93.

Lowe DG. Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol. 2. IEEE; 1999. p. 1150–7.

Wu L, Hoi SC, Yu N. Semantics-preserving bag-of-words models and applications. IEEE Trans Image Process. 2010;19(7):1908–20.

Article   MathSciNet   MATH   Google Scholar  

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

Yao G, Lei T, Zhong J. A review of convolutional-neural-network-based action recognition. Pattern Recogn Lett. 2019;118:14–22.

Dhillon A, Verma GK. Convolutional neural network: a review of models, methodologies and applications to object detection. Prog Artif Intell. 2020;9(2):85–112.

Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev. 2020;53(8):5455–516.

Hasan RI, Yusuf SM, Alzubaidi L. Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion. Plants. 2020;9(10):1302.

Xiao Y, Tian Z, Yu J, Zhang Y, Liu S, Du S, Lan X. A review of object detection based on deep learning. Multimed Tools Appl. 2020;79(33):23729–91.

Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2017;6:9375–89.

Zhang Z, Cui P, Zhu W. Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng. 2020. https://doi.org/10.1109/TKDE.2020.2981333 .

Shrestha A, Mahmood A. Review of deep learning algorithms and architectures. IEEE Access. 2019;7:53040–65.

Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. J Big Data. 2015;2(1):1.

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning, vol. 1. Cambridge: MIT press; 2016.

MATH   Google Scholar  

Shorten C, Khoshgoftaar TM, Furht B. Deep learning applications for COVID-19. J Big Data. 2021;8(1):1–54.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.

Bhowmick S, Nagarajaiah S, Veeraraghavan A. Vision and deep learning-based algorithms to detect and quantify cracks on concrete surfaces from uav videos. Sensors. 2020;20(21):6299.

Goh GB, Hodas NO, Vishnu A. Deep learning for computational chemistry. J Comput Chem. 2017;38(16):1291–307.

Li Y, Zhang T, Sun S, Gao X. Accelerating flash calculation through deep learning methods. J Comput Phys. 2019;394:153–65.

Yang W, Zhang X, Tian Y, Wang W, Xue JH, Liao Q. Deep learning for single image super-resolution: a brief review. IEEE Trans Multimed. 2019;21(12):3106–21.

Tang J, Li S, Liu P. A review of lane detection methods based on deep learning. Pattern Recogn. 2020;111:107623.

Zhao ZQ, Zheng P, Xu ST, Wu X. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019;30(11):3212–32.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–8.

Ng A. Machine learning yearning: technical strategy for AI engineers in the era of deep learning. 2019. https://www.mlyearning.org .

Metz C. Turing award won by 3 pioneers in artificial intelligence. The New York Times. 2019;27.

Nevo S, Anisimov V, Elidan G, El-Yaniv R, Giencke P, Gigi Y, Hassidim A, Moshe Z, Schlesinger M, Shalev G, et al. Ml for flood forecasting at scale; 2019. arXiv preprint arXiv:1901.09583 .

Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–50.

Benhammou Y, Achchab B, Herrera F, Tabik S. Breakhis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing. 2020;375:9–24.

Wulczyn E, Steiner DF, Xu Z, Sadhwani A, Wang H, Flament-Auvigne I, Mermel CH, Chen PHC, Liu Y, Stumpe MC. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS ONE. 2020;15(6):e0233678.

Nagpal K, Foote D, Liu Y, Chen PHC, Wulczyn E, Tan F, Olson N, Smith JL, Mohtashamian A, Wren JH, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2019;2(1):1–10.

Google Scholar  

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed. 2020;196(105):608.

Jamshidi M, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, La Spada L, Mirmozafari M, Dehghani M, et al. Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access. 2020;8:109581–95.

Shorfuzzaman M, Hossain MS. Metacovid: a siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients. Pattern Recogn. 2020;113:107700.

Carvelli L, Olesen AN, Brink-Kjær A, Leary EB, Peppard PE, Mignot E, Sørensen HB, Jennum P. Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts. Sleep Med. 2020;69:109–19.

De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, Askham H, Glorot X, O’Donoghue B, Visentin D, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342–50.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.

Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–31.

Van Essen B, Kim H, Pearce R, Boakye K, Chen B. Lbann: livermore big artificial neural network HPC toolkit. In: Proceedings of the workshop on machine learning in high-performance computing environments; 2015. p. 1–6.

Saeed MM, Al Aghbari Z, Alsharidah M. Big data clustering techniques based on spark: a literature review. PeerJ Comput Sci. 2020;6:321.

Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, et al. Human-level control through deep reinforcement learning. Nature. 2015;518(7540):529–33.

Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. Deep reinforcement learning: a brief survey. IEEE Signal Process Mag. 2017;34(6):26–38.

Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing; 2013. p. 1631–42.

Goller C, Kuchler A. Learning task-dependent distributed representations by backpropagation through structure. In: Proceedings of international conference on neural networks (ICNN’96), vol 1. IEEE; 1996. p. 347–52.

Socher R, Lin CCY, Ng AY, Manning CD. Parsing natural scenes and natural language with recursive neural networks. In: ICML; 2011.

Louppe G, Cho K, Becot C, Cranmer K. QCD-aware recursive neural networks for jet physics. J High Energy Phys. 2019;2019(1):57.

Sadr H, Pedram MM, Teshnehlab M. A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural Process Lett. 2019;50(3):2745–61.

Urban G, Subrahmanya N, Baldi P. Inner and outer recursive neural networks for chemoinformatics applications. J Chem Inf Model. 2018;58(2):207–11.

Hewamalage H, Bergmeir C, Bandara K. Recurrent neural networks for time series forecasting: current status and future directions. Int J Forecast. 2020;37(1):388–427.

Jiang Y, Kim H, Asnani H, Kannan S, Oh S, Viswanath P. Learn codes: inventing low-latency codes via recurrent neural networks. IEEE J Sel Areas Inf Theory. 2020;1(1):207–16.

John RA, Acharya J, Zhu C, Surendran A, Bose SK, Chaturvedi A, Tiwari N, Gao Y, He Y, Zhang KK, et al. Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks. Nat Commun. 2020;11(1):1–9.

Batur Dinler Ö, Aydin N. An optimal feature parameter set based on gated recurrent unit recurrent neural networks for speech segment detection. Appl Sci. 2020;10(4):1273.

Jagannatha AN, Yu H. Structured prediction models for RNN based sequence labeling in clinical text. In: Proceedings of the conference on empirical methods in natural language processing. conference on empirical methods in natural language processing, vol. 2016, NIH Public Access; 2016. p. 856.

Pascanu R, Gulcehre C, Cho K, Bengio Y. How to construct deep recurrent neural networks. In: Proceedings of the second international conference on learning representations (ICLR 2014); 2014.

Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics; 2010. p. 249–56.

Gao C, Yan J, Zhou S, Varshney PK, Liu H. Long short-term memory-based deep recurrent neural networks for target tracking. Inf Sci. 2019;502:279–96.

Zhou DX. Theory of deep convolutional neural networks: downsampling. Neural Netw. 2020;124:319–27.

Article   MATH   Google Scholar  

Jhong SY, Tseng PY, Siriphockpirom N, Hsia CH, Huang MS, Hua KL, Chen YY. An automated biometric identification system using CNN-based palm vein recognition. In: 2020 international conference on advanced robotics and intelligent systems (ARIS). IEEE; 2020. p. 1–6.

Al-Azzawi A, Ouadou A, Max H, Duan Y, Tanner JJ, Cheng J. Deepcryopicker: fully automated deep neural network for single protein particle picking in cryo-EM. BMC Bioinform. 2020;21(1):1–38.

Wang T, Lu C, Yang M, Hong F, Liu C. A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. PeerJ Comput Sci. 2020;6:324.

Li G, Zhang M, Li J, Lv F, Tong G. Efficient densely connected convolutional neural networks. Pattern Recogn. 2021;109:107610.

Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77.

Fang W, Love PE, Luo H, Ding L. Computer vision for behaviour-based safety in construction: a review and future directions. Adv Eng Inform. 2020;43:100980.

Palaz D, Magimai-Doss M, Collobert R. End-to-end acoustic modeling using convolutional neural networks for hmm-based automatic speech recognition. Speech Commun. 2019;108:15–32.

Li HC, Deng ZY, Chiang HH. Lightweight and resource-constrained learning network for face recognition with performance optimization. Sensors. 2020;20(21):6114.

Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol. 1962;160(1):106.

Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift; 2015. arXiv preprint arXiv:1502.03167 .

Ruder S. An overview of gradient descent optimization algorithms; 2016. arXiv preprint arXiv:1609.04747 .

Bottou L. Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010. Springer; 2010. p. 177–86.

Hinton G, Srivastava N, Swersky K. Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited on. 2012;14(8).

Zhang Z. Improved Adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS). IEEE; 2018. p. 1–2.

Alzubaidi L, Fadhel MA, Al-Shamma O, Zhang J, Duan Y. Deep learning models for classification of red blood cells in microscopy images to aid in sickle cell anemia diagnosis. Electronics. 2020;9(3):427.

Alzubaidi L, Fadhel MA, Al-Shamma O, Zhang J, Santamaría J, Duan Y, Oleiwi SR. Towards a better understanding of transfer learning for medical imaging: a case study. Appl Sci. 2020;10(13):4523.

Alzubaidi L, Al-Shamma O, Fadhel MA, Farhan L, Zhang J, Duan Y. Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model. Electronics. 2020;9(3):445.

LeCun Y, Jackel LD, Bottou L, Cortes C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P, et al. Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw Stat Mech Perspect. 1995;261:276.

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–58.

MathSciNet   MATH   Google Scholar  

Dahl GE, Sainath TN, Hinton GE. Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE; 2013. p. 8609–13.

Xu B, Wang N, Chen T, Li M. Empirical evaluation of rectified activations in convolutional network; 2015. arXiv preprint arXiv:1505.00853 .

Hochreiter S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst. 1998;6(02):107–16.

Lin M, Chen Q, Yan S. Network in network; 2013. arXiv preprint arXiv:1312.4400 .

Hsiao TY, Chang YC, Chou HH, Chiu CT. Filter-based deep-compression with global average pooling for convolutional networks. J Syst Arch. 2019;95:9–18.

Li Z, Wang SH, Fan RR, Cao G, Zhang YD, Guo T. Teeth category classification via seven-layer deep convolutional neural network with max pooling and global average pooling. Int J Imaging Syst Technol. 2019;29(4):577–83.

Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer; 2014. p. 818–33.

Erhan D, Bengio Y, Courville A, Vincent P. Visualizing higher-layer features of a deep network. Univ Montreal. 2009;1341(3):1.

Le QV. Building high-level features using large scale unsupervised learning. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE; 2013. p. 8595–8.

Grün F, Rupprecht C, Navab N, Tombari F. A taxonomy and library for visualizing learned features in convolutional neural networks; 2016. arXiv preprint arXiv:1606.07757 .

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition; 2014. arXiv preprint arXiv:1409.1556 .

Ranzato M, Huang FJ, Boureau YL, LeCun Y. Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: 2007 IEEE conference on computer vision and pattern recognition. IEEE; 2007. p. 1–8.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 1–9.

Bengio Y, et al. Rmsprop and equilibrated adaptive learning rates for nonconvex optimization; 2015. arXiv:1502.04390 corr abs/1502.04390

Srivastava RK, Greff K, Schmidhuber J. Highway networks; 2015. arXiv preprint arXiv:1505.00387 .

Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid. 2017;10(1):841–51.

Ordóñez FJ, Roggen D. Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors. 2016;16(1):115.

CireşAn D, Meier U, Masci J, Schmidhuber J. Multi-column deep neural network for traffic sign classification. Neural Netw. 2012;32:333–8.

Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning; 2016. arXiv preprint arXiv:1602.07261 .

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2818–26.

Wu S, Zhong S, Liu Y. Deep residual learning for image steganalysis. Multimed Tools Appl. 2018;77(9):10437–53.

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 4700–08.

Rubin J, Parvaneh S, Rahman A, Conroy B, Babaeizadeh S. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. J Electrocardiol. 2018;51(6):S18-21.

Kuang P, Ma T, Chen Z, Li F. Image super-resolution with densely connected convolutional networks. Appl Intell. 2019;49(1):125–36.

Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 1492–500.

Su A, He X, Zhao X. Jpeg steganalysis based on ResNeXt with gauss partial derivative filters. Multimed Tools Appl. 2020;80(3):3349–66.

Yadav D, Jalal A, Garlapati D, Hossain K, Goyal A, Pant G. Deep learning-based ResNeXt model in phycological studies for future. Algal Res. 2020;50:102018.

Han W, Feng R, Wang L, Gao L. Adaptive spatial-scale-aware deep convolutional neural network for high-resolution remote sensing imagery scene classification. In: IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium. IEEE; 2018. p. 4736–9.

Zagoruyko S, Komodakis N. Wide residual networks; 2016. arXiv preprint arXiv:1605.07146 .

Huang G, Sun Y, Liu Z, Sedra D, Weinberger KQ. Deep networks with stochastic depth. In: European conference on computer vision. Springer; 2016. p. 646–61.

Huynh HT, Nguyen H. Joint age estimation and gender classification of Asian faces using wide ResNet. SN Comput Sci. 2020;1(5):1–9.

Takahashi R, Matsubara T, Uehara K. Data augmentation using random image cropping and patching for deep cnns. IEEE Trans Circuits Syst Video Technol. 2019;30(9):2917–31.

Han D, Kim J, Kim J. Deep pyramidal residual networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 5927–35.

Wang Y, Wang L, Wang H, Li P. End-to-end image super-resolution via deep and shallow convolutional networks. IEEE Access. 2019;7:31959–70.

Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 1251–8.

Lo WW, Yang X, Wang Y. An xception convolutional neural network for malware classification with transfer learning. In: 2019 10th IFIP international conference on new technologies, mobility and security (NTMS). IEEE; 2019. p. 1–5.

Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of xception and resnet50v2. Inform Med Unlocked. 2020;19:100360.

Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X. Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 3156–64.

Salakhutdinov R, Larochelle H. Efficient learning of deep boltzmann machines. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics; 2010. p. 693–700.

Goh H, Thome N, Cord M, Lim JH. Top-down regularization of deep belief networks. Adv Neural Inf Process Syst. 2013;26:1878–86.

Guan J, Lai R, Xiong A, Liu Z, Gu L. Fixed pattern noise reduction for infrared images based on cascade residual attention CNN. Neurocomputing. 2020;377:301–13.

Bi Q, Qin K, Zhang H, Li Z, Xu K. RADC-Net: a residual attention based convolution network for aerial scene classification. Neurocomputing. 2020;377:345–59.

Jaderberg M, Simonyan K, Zisserman A, et al. Spatial transformer networks. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2015. p. 2017–25.

Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. p. 7132–41.

Mou L, Zhu XX. Learning to pay attention on spectral domain: a spectral attention module-based convolutional network for hyperspectral image classification. IEEE Trans Geosci Remote Sens. 2019;58(1):110–22.

Woo S, Park J, Lee JY, So Kweon I. CBAM: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV); 2018. p. 3–19.

Roy AG, Navab N, Wachinger C. Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. Springer; 2018. p. 421–9.

Roy AG, Navab N, Wachinger C. Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation’’ blocks. IEEE Trans Med Imaging. 2018;38(2):540–9.

Sabour S, Frosst N, Hinton GE. Dynamic routing between capsules. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2017. p. 3856–66.

Arun P, Buddhiraju KM, Porwal A. Capsulenet-based spatial-spectral classifier for hyperspectral images. IEEE J Sel Topics Appl Earth Obs Remote Sens. 2019;12(6):1849–65.

Xinwei L, Lianghao X, Yi Y. Compact video fingerprinting via an improved capsule net. Syst Sci Control Eng. 2020;9:1–9.

Ma B, Li X, Xia Y, Zhang Y. Autonomous deep learning: a genetic DCNN designer for image classification. Neurocomputing. 2020;379:152–61.

Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X, et al. Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2020. https://doi.org/10.1109/TPAMI.2020.2983686 .

Cheng B, Xiao B, Wang J, Shi H, Huang TS, Zhang L. Higherhrnet: scale-aware representation learning for bottom-up human pose estimation. In: CVPR 2020; 2020. https://www.microsoft.com/en-us/research/publication/higherhrnet-scale-aware-representation-learning-for-bottom-up-human-pose-estimation/ .

Karimi H, Derr T, Tang J. Characterizing the decision boundary of deep neural networks; 2019. arXiv preprint arXiv:1912.11460 .

Li Y, Ding L, Gao X. On the decision boundary of deep neural networks; 2018. arXiv preprint arXiv:1808.05385 .

Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2014. p. 3320–8.

Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In: International conference on artificial neural networks. Springer; 2018. p. 270–9.

Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. 2016;3(1):9.

Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):60.

Wang F, Wang H, Wang H, Li G, Situ G. Learning from simulation: an end-to-end deep-learning approach for computational ghost imaging. Opt Express. 2019;27(18):25560–72.

Pan W. A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing. 2016;177:447–53.

Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE; 2009. p. 248–55.

Cook D, Feuz KD, Krishnan NC. Transfer learning for activity recognition: a survey. Knowl Inf Syst. 2013;36(3):537–56.

Cao X, Wang Z, Yan P, Li X. Transfer learning for pedestrian detection. Neurocomputing. 2013;100:51–7.

Raghu M, Zhang C, Kleinberg J, Bengio S. Transfusion: understanding transfer learning for medical imaging. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2019. p. 3347–57.

Pham TN, Van Tran L, Dao SVT. Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access. 2020;8:189960–73.

Saleh AM, Hamoud T. Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation. J Big Data. 2021;8(1):1–20.

Hirahara D, Takaya E, Takahara T, Ueda T. Effects of data count and image scaling on deep learning training. PeerJ Comput Sci. 2020;6:312.

Moreno-Barea FJ, Strazzera F, Jerez JM, Urda D, Franco L. Forward noise adjustment scheme for data augmentation. In: 2018 IEEE symposium series on computational intelligence (SSCI). IEEE; 2018. p. 728–34.

Dua D, Karra Taniskidou E. Uci machine learning repository. Irvine: University of california. School of Information and Computer Science; 2017. http://archive.ics.uci.edu/ml

Johnson JM, Khoshgoftaar TM. Survey on deep learning with class imbalance. J Big Data. 2019;6(1):27.

Yang P, Zhang Z, Zhou BB, Zomaya AY. Sample subset optimization for classifying imbalanced biological data. In: Pacific-Asia conference on knowledge discovery and data mining. Springer; 2011. p. 333–44.

Yang P, Yoo PD, Fernando J, Zhou BB, Zhang Z, Zomaya AY. Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications. IEEE Trans Cybern. 2013;44(3):445–55.

Wang S, Sun S, Xu J. Auc-maximized deep convolutional neural fields for sequence labeling 2015. arXiv preprint arXiv:1511.05265 .

Li Y, Wang S, Umarov R, Xie B, Fan M, Li L, Gao X. Deepre: sequence-based enzyme EC number prediction by deep learning. Bioinformatics. 2018;34(5):760–9.

Li Y, Huang C, Ding L, Li Z, Pan Y, Gao X. Deep learning in bioinformatics: introduction, application, and perspective in the big data era. Methods. 2019;166:4–21.

Choi E, Bahadori MT, Sun J, Kulas J, Schuetz A, Stewart W. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2016. p. 3504–12.

Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141):20170,387.

Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12(10):931–4.

Pokuri BSS, Ghosal S, Kokate A, Sarkar S, Ganapathysubramanian B. Interpretable deep learning for guided microstructure-property explorations in photovoltaics. NPJ Comput Mater. 2019;5(1):1–11.

Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016. p. 1135–44.

Wang L, Nie R, Yu Z, Xin R, Zheng C, Zhang Z, Zhang J, Cai J. An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data. Nat Mach Intell. 2020;2(11):1–11.

Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks; 2017. arXiv preprint arXiv:1703.01365 .

Platt J, et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif. 1999;10(3):61–74.

Nair T, Precup D, Arnold DL, Arbel T. Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med Image Anal. 2020;59:101557.

Herzog L, Murina E, Dürr O, Wegener S, Sick B. Integrating uncertainty in deep neural networks for MRI based stroke analysis. Med Image Anal. 2020;65:101790.

Pereyra G, Tucker G, Chorowski J, Kaiser Ł, Hinton G. Regularizing neural networks by penalizing confident output distributions; 2017. arXiv preprint arXiv:1701.06548 .

Naeini MP, Cooper GF, Hauskrecht M. Obtaining well calibrated probabilities using bayesian binning. In: Proceedings of the... AAAI conference on artificial intelligence. AAAI conference on artificial intelligence, vol. 2015. NIH Public Access; 2015. p. 2901.

Li M, Sethi IK. Confidence-based classifier design. Pattern Recogn. 2006;39(7):1230–40.

Zadrozny B, Elkan C. Obtaining calibrated probability estimates from decision trees and Naive Bayesian classifiers. In: ICML, vol. 1, Citeseer; 2001. p. 609–16.

Steinwart I. Consistency of support vector machines and other regularized kernel classifiers. IEEE Trans Inf Theory. 2005;51(1):128–42.

Lee K, Lee K, Shin J, Lee H. Overcoming catastrophic forgetting with unlabeled data in the wild. In: Proceedings of the IEEE international conference on computer vision; 2019. p. 312–21.

Shmelkov K, Schmid C, Alahari K. Incremental learning of object detectors without catastrophic forgetting. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 3400–09.

Zenke F, Gerstner W, Ganguli S. The temporal paradox of Hebbian learning and homeostatic plasticity. Curr Opin Neurobiol. 2017;43:166–76.

Andersen N, Krauth N, Nabavi S. Hebbian plasticity in vivo: relevance and induction. Curr Opin Neurobiol. 2017;45:188–92.

Zheng R, Chakraborti S. A phase ii nonparametric adaptive exponentially weighted moving average control chart. Qual Eng. 2016;28(4):476–90.

Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH. ICARL: Incremental classifier and representation learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 2001–10.

Hinton GE, Plaut DC. Using fast weights to deblur old memories. In: Proceedings of the ninth annual conference of the cognitive science society; 1987. p. 177–86.

Parisi GI, Kemker R, Part JL, Kanan C, Wermter S. Continual lifelong learning with neural networks: a review. Neural Netw. 2019;113:54–71.

Soltoggio A, Stanley KO, Risi S. Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks. Neural Netw. 2018;108:48–67.

Parisi GI, Tani J, Weber C, Wermter S. Lifelong learning of human actions with deep neural network self-organization. Neural Netw. 2017;96:137–49.

Cheng Y, Wang D, Zhou P, Zhang T. Model compression and acceleration for deep neural networks: the principles, progress, and challenges. IEEE Signal Process Mag. 2018;35(1):126–36.

Wiedemann S, Kirchhoffer H, Matlage S, Haase P, Marban A, Marinč T, Neumann D, Nguyen T, Schwarz H, Wiegand T, et al. Deepcabac: a universal compression algorithm for deep neural networks. IEEE J Sel Topics Signal Process. 2020;14(4):700–14.

Mehta N, Pandit A. Concurrence of big data analytics and healthcare: a systematic review. Int J Med Inform. 2018;114:57–65.

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9.

Shawahna A, Sait SM, El-Maleh A. Fpga-based accelerators of deep learning networks for learning and classification: a review. IEEE Access. 2018;7:7823–59.

Min Z. Public welfare organization management system based on FPGA and deep learning. Microprocess Microsyst. 2020;80:103333.

Al-Shamma O, Fadhel MA, Hameed RA, Alzubaidi L, Zhang J. Boosting convolutional neural networks performance based on fpga accelerator. In: International conference on intelligent systems design and applications. Springer; 2018. p. 509–17.

Han S, Mao H, Dally WJ. Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding; 2015. arXiv preprint arXiv:1510.00149 .

Chen Z, Zhang L, Cao Z, Guo J. Distilling the knowledge from handcrafted features for human activity recognition. IEEE Trans Ind Inform. 2018;14(10):4334–42.

Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network; 2015. arXiv preprint arXiv:1503.02531 .

Lenssen JE, Fey M, Libuschewski P. Group equivariant capsule networks. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2018. p. 8844–53.

Denton EL, Zaremba W, Bruna J, LeCun Y, Fergus R. Exploiting linear structure within convolutional networks for efficient evaluation. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2014. p. 1269–77.

Xu Q, Zhang M, Gu Z, Pan G. Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs. Neurocomputing. 2019;328:69–74.

Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding deep learning requires rethinking generalization. Commun ACM. 2018;64(3):107–15.

Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering. 2020;6(10):1122–9.

Sharma K, Alsadoon A, Prasad P, Al-Dala’in T, Nguyen TQV, Pham DTH. A novel solution of using deep learning for left ventricle detection: enhanced feature extraction. Comput Methods Programs Biomed. 2020;197:105751.

Zhang G, Wang C, Xu B, Grosse R. Three mechanisms of weight decay regularization; 2018. arXiv preprint arXiv:1810.12281 .

Laurent C, Pereyra G, Brakel P, Zhang Y, Bengio Y. Batch normalized recurrent neural networks. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE; 2016. p. 2657–61.

Salamon J, Bello JP. Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process Lett. 2017;24(3):279–83.

Wang X, Qin Y, Wang Y, Xiang S, Chen H. ReLTanh: an activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis. Neurocomputing. 2019;363:88–98.

Tan HH, Lim KH. Vanishing gradient mitigation with deep learning neural network optimization. In: 2019 7th international conference on smart computing & communications (ICSCC). IEEE; 2019. p. 1–4.

MacDonald G, Godbout A, Gillcash B, Cairns S. Volume-preserving neural networks: a solution to the vanishing gradient problem; 2019. arXiv preprint arXiv:1911.09576 .

Mittal S, Vaishay S. A survey of techniques for optimizing deep learning on GPUs. J Syst Arch. 2019;99:101635.

Kanai S, Fujiwara Y, Iwamura S. Preventing gradient explosions in gated recurrent units. In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2017. p. 435–44.

Hanin B. Which neural net architectures give rise to exploding and vanishing gradients? In: Advances in neural information processing systems. San Mateo: Morgan Kaufmann Publishers; 2018. p. 582–91.

Ribeiro AH, Tiels K, Aguirre LA, Schön T. Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness. In: International conference on artificial intelligence and statistics, PMLR; 2020. p. 2370–80.

D’Amour A, Heller K, Moldovan D, Adlam B, Alipanahi B, Beutel A, Chen C, Deaton J, Eisenstein J, Hoffman MD, et al. Underspecification presents challenges for credibility in modern machine learning; 2020. arXiv preprint arXiv:2011.03395 .

Chea P, Mandell JC. Current applications and future directions of deep learning in musculoskeletal radiology. Skelet Radiol. 2020;49(2):1–15.

Wu X, Sahoo D, Hoi SC. Recent advances in deep learning for object detection. Neurocomputing. 2020;396:39–64.

Kuutti S, Bowden R, Jin Y, Barber P, Fallah S. A survey of deep learning applications to autonomous vehicle control. IEEE Trans Intell Transp Syst. 2020;22:712–33.

Yolcu G, Oztel I, Kazan S, Oz C, Bunyak F. Deep learning-based face analysis system for monitoring customer interest. J Ambient Intell Humaniz Comput. 2020;11(1):237–48.

Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R. A survey of deep learning-based object detection. IEEE Access. 2019;7:128837–68.

Muhammad K, Khan S, Del Ser J, de Albuquerque VHC. Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn Syst. 2020;32:507–22.

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

Mukherjee D, Mondal R, Singh PK, Sarkar R, Bhattacharjee D. Ensemconvnet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications. Multimed Tools Appl. 2020;79(41):31663–90.

Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, Parmar C, Alvi RM, Banerji D, Uno M, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nature Commun. 2021;12(1):1–9.

Wang J, Liu Q, Xie H, Yang Z, Zhou H. Boosted efficientnet: detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers. 2021;13(4):661.

Yu H, Yang LT, Zhang Q, Armstrong D, Deen MJ. Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing. 2021. https://doi.org/10.1016/j.neucom.2020.04.157 .

Bharati S, Podder P, Mondal MRH. Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlocked. 2020;20:100391.

Dong Y, Pan Y, Zhang J, Xu W. Learning to read chest X-ray images from 16000+ examples using CNN. In: 2017 IEEE/ACM international conference on connected health: applications, systems and engineering technologies (CHASE). IEEE; 2017. p. 51–7.

Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J. High-throughput classification of radiographs using deep convolutional neural networks. J Digit Imaging. 2017;30(1):95–101.

Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, et al. Chexnet: radiologist-level pneumonia detection on chest X-rays with deep learning; 2017. arXiv preprint arXiv:1711.05225 .

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 2097–106.

Zuo W, Zhou F, Li Z, Wang L. Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access. 2019;7:32510–21.

Shen W, Zhou M, Yang F, Yang C, Tian J. Multi-scale convolutional neural networks for lung nodule classification. In: International conference on information processing in medical imaging. Springer; 2015. p. 588–99.

Li R, Zhang W, Suk HI, Wang L, Li J, Shen D, Ji S. Deep learning based imaging data completion for improved brain disease diagnosis. In: International conference on medical image computing and computer-assisted intervention. Springer; 2014. p. 305–12.

Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O, et al. Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med Image Anal. 2020;63:101694.

Mehmood A, Maqsood M, Bashir M, Shuyuan Y. A deep siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci. 2020;10(2):84.

Hosseini-Asl E, Ghazal M, Mahmoud A, Aslantas A, Shalaby A, Casanova M, Barnes G, Gimel’farb G, Keynton R, El-Baz A. Alzheimer’s disease diagnostics by a 3d deeply supervised adaptable convolutional network. Front Biosci. 2018;23:584–96.

Korolev S, Safiullin A, Belyaev M, Dodonova Y. Residual and plain convolutional neural networks for 3D brain MRI classification. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE; 2017. p. 835–8.

Alzubaidi L, Fadhel MA, Oleiwi SR, Al-Shamma O, Zhang J. DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimed Tools Appl. 2020;79(21):15655–77.

Goyal M, Reeves ND, Davison AK, Rajbhandari S, Spragg J, Yap MH. Dfunet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans Emerg Topics Comput Intell. 2018;4(5):728–39.

Yap MH., Hachiuma R, Alavi A, Brungel R, Goyal M, Zhu H, Cassidy B, Ruckert J, Olshansky M, Huang X, et al. Deep learning in diabetic foot ulcers detection: a comprehensive evaluation; 2020. arXiv preprint arXiv:2010.03341 .

Tulloch J, Zamani R, Akrami M. Machine learning in the prevention, diagnosis and management of diabetic foot ulcers: a systematic review. IEEE Access. 2020;8:198977–9000.

Fadhel MA, Al-Shamma O, Alzubaidi L, Oleiwi SR. Real-time sickle cell anemia diagnosis based hardware accelerator. In: International conference on new trends in information and communications technology applications, Springer; 2020. p. 189–99.

Debelee TG, Kebede SR, Schwenker F, Shewarega ZM. Deep learning in selected cancers’ image analysis—a survey. J Imaging. 2020;6(11):121.

Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett. 2019;125:1–6.

Alzubaidi L, Hasan RI, Awad FH, Fadhel MA, Alshamma O, Zhang J. Multi-class breast cancer classification by a novel two-branch deep convolutional neural network architecture. In: 2019 12th international conference on developments in eSystems engineering (DeSE). IEEE; 2019. p. 268–73.

Roy K, Banik D, Bhattacharjee D, Nasipuri M. Patch-based system for classification of breast histology images using deep learning. Comput Med Imaging Gr. 2019;71:90–103.

Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, María Vanegas A. Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors. 2020;20(16):4373.

Hosny KM, Kassem MA, Foaud MM. Skin cancer classification using deep learning and transfer learning. In: 2018 9th Cairo international biomedical engineering conference (CIBEC). IEEE; 2018. p. 90–3.

Dorj UO, Lee KK, Choi JY, Lee M. The skin cancer classification using deep convolutional neural network. Multimed Tools Appl. 2018;77(8):9909–24.

Kassem MA, Hosny KM, Fouad MM. Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access. 2020;8:114822–32.

Heidari M, Mirniaharikandehei S, Khuzani AZ, Danala G, Qiu Y, Zheng B. Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int J Med Inform. 2020;144:104284.

Al-Timemy AH, Khushaba RN, Mosa ZM, Escudero J. An efficient mixture of deep and machine learning models for COVID-19 and tuberculosis detection using X-ray images in resource limited settings 2020. arXiv preprint arXiv:2007.08223 .

Abraham B, Nair MS. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier. Biocybern Biomed Eng. 2020;40(4):1436–45.

Nour M, Cömert Z, Polat K. A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Appl Soft Comput. 2020;97:106580.

Mallio CA, Napolitano A, Castiello G, Giordano FM, D’Alessio P, Iozzino M, Sun Y, Angeletti S, Russano M, Santini D, et al. Deep learning algorithm trained with COVID-19 pneumonia also identifies immune checkpoint inhibitor therapy-related pneumonitis. Cancers. 2021;13(4):652.

Fourcade A, Khonsari R. Deep learning in medical image analysis: a third eye for doctors. J Stomatol Oral Maxillofac Surg. 2019;120(4):279–88.

Guo Z, Li X, Huang H, Guo N, Li Q. Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans Radiat Plasma Med Sci. 2019;3(2):162–9.

Thakur N, Yoon H, Chong Y. Current trends of artificial intelligence for colorectal cancer pathology image analysis: a systematic review. Cancers. 2020;12(7):1884.

Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 2019;29(2):102–27.

Yadav SS, Jadhav SM. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data. 2019;6(1):113.

Nehme E, Freedman D, Gordon R, Ferdman B, Weiss LE, Alalouf O, Naor T, Orange R, Michaeli T, Shechtman Y. DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat Methods. 2020;17(7):734–40.

Zulkifley MA, Abdani SR, Zulkifley NH. Pterygium-Net: a deep learning approach to pterygium detection and localization. Multimed Tools Appl. 2019;78(24):34563–84.

Sirazitdinov I, Kholiavchenko M, Mustafaev T, Yixuan Y, Kuleev R, Ibragimov B. Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database. Comput Electr Eng. 2019;78:388–99.

Zhao W, Shen L, Han B, Yang Y, Cheng K, Toesca DA, Koong AC, Chang DT, Xing L. Markerless pancreatic tumor target localization enabled by deep learning. Int J Radiat Oncol Biol Phys. 2019;105(2):432–9.

Roth HR, Lee CT, Shin HC, Seff A, Kim L, Yao J, Lu L, Summers RM. Anatomy-specific classification of medical images using deep convolutional nets. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE; 2015. p. 101–4.

Shin HC, Orton MR, Collins DJ, Doran SJ, Leach MO. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data. IEEE Trans Pattern Anal Mach Intell. 2012;35(8):1930–43.

Li Z, Dong M, Wen S, Hu X, Zhou P, Zeng Z. CLU-CNNs: object detection for medical images. Neurocomputing. 2019;350:53–9.

Gao J, Jiang Q, Zhou B, Chen D. Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview. Math Biosci Eng. 2019;16(6):6536.

Article   MathSciNet   Google Scholar  

Lumini A, Nanni L. Review fair comparison of skin detection approaches on publicly available datasets. Expert Syst Appl. 2020. https://doi.org/10.1016/j.eswa.2020.113677 .

Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C, Damaševičius R, De Albuquerque VHC. A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci. 2020;10(2):559.

Apostolopoulos ID, Mpesiana TA. COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635–40.

Mahmud T, Rahman MA, Fattah SA. CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput Biol Med. 2020;122:103869.

Tayarani-N MH. Applications of artificial intelligence in battling against COVID-19: a literature review. Chaos Solitons Fractals. 2020;142:110338.

Toraman S, Alakus TB, Turkoglu I. Convolutional capsnet: a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals. 2020;140:110122.

Dascalu A, David E. Skin cancer detection by deep learning and sound analysis algorithms: a prospective clinical study of an elementary dermoscope. EBioMedicine. 2019;43:107–13.

Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev. 2020;54:1–31.

Zhang N, Cai YX, Wang YY, Tian YT, Wang XL, Badami B. Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med. 2020;102:101756.

Thurnhofer-Hemsi K, Domínguez E. A convolutional neural network framework for accurate skin cancer detection. Neural Process Lett. 2020. https://doi.org/10.1007/s11063-020-10364-y .

Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning. Nat Mach Intell. 2020;2(6):356–62.

Lei H, Liu S, Elazab A, Lei B. Attention-guided multi-branch convolutional neural network for mitosis detection from histopathological images. IEEE J Biomed Health Inform. 2020;25(2):358–70.

Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR. Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recogn Lett. 2020;133:232–9.

Sebai M, Wang X, Wang T. Maskmitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images. Med Biol Eng Comput. 2020;58:1603–23.

Sebai M, Wang T, Al-Fadhli SA. Partmitosis: a partially supervised deep learning framework for mitosis detection in breast cancer histopathology images. IEEE Access. 2020;8:45133–47.

Mahmood T, Arsalan M, Owais M, Lee MB, Park KR. Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs. J Clin Med. 2020;9(3):749.

Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: a survey. Med Image Anal. 2020;67:101813.

Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer; 2013. p. 411–8.

Sirinukunwattana K, Raza SEA, Tsang YW, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1196–206.

Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging. 2015;35(1):119–30.

Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N. Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1313–21.

Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF. Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J Image Video Process. 2018;2018(1):97.

Thaha MM, Kumar KPM, Murugan B, Dhanasekeran S, Vijayakarthick P, Selvi AS. Brain tumor segmentation using convolutional neural networks in MRI images. J Med Syst. 2019;43(9):294.

Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR. Convolutional neural networks for multi-class brain disease detection using MRI images. Comput Med Imaging Gr. 2019;78:101673.

Gabr RE, Coronado I, Robinson M, Sujit SJ, Datta S, Sun X, Allen WJ, Lublin FD, Wolinsky JS, Narayana PA. Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: a large-scale study. Mult Scler J. 2020;26(10):1217–26.

Chen S, Ding C, Liu M. Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recogn. 2019;88:90–100.

Hu K, Gan Q, Zhang Y, Deng S, Xiao F, Huang W, Cao C, Gao X. Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access. 2019;7:92615–29.

Wadhwa A, Bhardwaj A, Verma VS. A review on brain tumor segmentation of MRI images. Magn Reson Imaging. 2019;61:247–59.

Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–59.

Moeskops P, Viergever MA, Mendrik AM, De Vries LS, Benders MJ, Išgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging. 2016;35(5):1252–61.

Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE; 2016. p. 565–71.

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer; 2015. p. 234–41.

Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. 2016;35(5):1240–51.

Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18–31.

Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2017;40(4):834–48.

Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, Shi Q, Jin S, Zhang L, You Z. COVID-19 chest CT image segmentation—a deep convolutional neural network solution; 2020. arXiv preprint arXiv:2004.10987 .

Wang G, Liu X, Li C, Xu Z, Ruan J, Zhu H, Meng T, Li K, Huang N, Zhang S. A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging. 2020;39(8):2653–63.

Khan SH, Sohail A, Khan A, Lee YS. Classification and region analysis of COVID-19 infection using lung CT images and deep convolutional neural networks; 2020. arXiv preprint arXiv:2009.08864 .

Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev Biomed Eng. 2020;14:4–5.

Santamaría J, Rivero-Cejudo M, Martos-Fernández M, Roca F. An overview on the latest nature-inspired and metaheuristics-based image registration algorithms. Appl Sci. 2020;10(6):1928.

Santamaría J, Cordón O, Damas S. A comparative study of state-of-the-art evolutionary image registration methods for 3D modeling. Comput Vision Image Underst. 2011;115(9):1340–54.

Yumer ME, Mitra NJ. Learning semantic deformation flows with 3D convolutional networks. In: European conference on computer vision. Springer; 2016. p. 294–311.

Ding L, Feng C. Deepmapping: unsupervised map estimation from multiple point clouds. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2019. p. 8650–9.

Mahadevan S. Imagination machines: a new challenge for artificial intelligence. AAAI. 2018;2018:7988–93.

Wang L, Fang Y. Unsupervised 3D reconstruction from a single image via adversarial learning; 2017. arXiv preprint arXiv:1711.09312 .

Hermoza R, Sipiran I. 3D reconstruction of incomplete archaeological objects using a generative adversarial network. In: Proceedings of computer graphics international 2018. Association for Computing Machinery; 2018. p. 5–11.

Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. Deep learning in medical image registration: a review. Phys Med Biol. 2020;65(20):20TR01.

Haskins G, Kruger U, Yan P. Deep learning in medical image registration: a survey. Mach Vision Appl. 2020;31(1):8.

de Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Išgum I. A deep learning framework for unsupervised affine and deformable image registration. Med Image Anal. 2019;52:128–43.

Yang X, Kwitt R, Styner M, Niethammer M. Quicksilver: fast predictive image registration—a deep learning approach. NeuroImage. 2017;158:378–96.

Miao S, Wang ZJ, Liao R. A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging. 2016;35(5):1352–63.

Li P, Pei Y, Guo Y, Ma G, Xu T, Zha H. Non-rigid 2D–3D registration using convolutional autoencoders. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE; 2020. p. 700–4.

Zhang J, Yeung SH, Shu Y, He B, Wang W. Efficient memory management for GPU-based deep learning systems; 2019. arXiv preprint arXiv:1903.06631 .

Zhao H, Han Z, Yang Z, Zhang Q, Yang F, Zhou L, Yang M, Lau FC, Wang Y, Xiong Y, et al. Hived: sharing a {GPU} cluster for deep learning with guarantees. In: 14th {USENIX} symposium on operating systems design and implementation ({OSDI} 20); 2020. p. 515–32.

Lin Y, Jiang Z, Gu J, Li W, Dhar S, Ren H, Khailany B, Pan DZ. DREAMPlace: deep learning toolkit-enabled GPU acceleration for modern VLSI placement. IEEE Trans Comput Aided Des Integr Circuits Syst. 2020;40:748–61.

Hossain S, Lee DJ. Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors. 2019;19(15):3371.

Castro FM, Guil N, Marín-Jiménez MJ, Pérez-Serrano J, Ujaldón M. Energy-based tuning of convolutional neural networks on multi-GPUs. Concurr Comput Pract Exp. 2019;31(21):4786.

Gschwend D. Zynqnet: an fpga-accelerated embedded convolutional neural network; 2020. arXiv preprint arXiv:2005.06892 .

Zhang N, Wei X, Chen H, Liu W. FPGA implementation for CNN-based optical remote sensing object detection. Electronics. 2021;10(3):282.

Zhao M, Hu C, Wei F, Wang K, Wang C, Jiang Y. Real-time underwater image recognition with FPGA embedded system for convolutional neural network. Sensors. 2019;19(2):350.

Liu X, Yang J, Zou C, Chen Q, Yan X, Chen Y, Cai C. Collaborative edge computing with FPGA-based CNN accelerators for energy-efficient and time-aware face tracking system. IEEE Trans Comput Soc Syst. 2021. https://doi.org/10.1109/TCSS.2021.3059318 .

Hossin M, Sulaiman M. A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process. 2015;5(2):1.

Provost F, Domingos P. Tree induction for probability-based ranking. Mach Learn. 2003;52(3):199–215.

Rakotomamonyj A. Optimizing area under roc with SVMS. In: Proceedings of the European conference on artificial intelligence workshop on ROC curve and artificial intelligence (ROCAI 2004), 2004. p. 71–80.

Mingote V, Miguel A, Ortega A, Lleida E. Optimization of the area under the roc curve using neural network supervectors for text-dependent speaker verification. Comput Speech Lang. 2020;63:101078.

Fawcett T. An introduction to roc analysis. Pattern Recogn Lett. 2006;27(8):861–74.

Huang J, Ling CX. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng. 2005;17(3):299–310.

Hand DJ, Till RJ. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn. 2001;45(2):171–86.

Masoudnia S, Mersa O, Araabi BN, Vahabie AH, Sadeghi MA, Ahmadabadi MN. Multi-representational learning for offline signature verification using multi-loss snapshot ensemble of CNNs. Expert Syst Appl. 2019;133:317–30.

Coupé P, Mansencal B, Clément M, Giraud R, de Senneville BD, Ta VT, Lepetit V, Manjon JV. Assemblynet: a large ensemble of CNNs for 3D whole brain MRI segmentation. NeuroImage. 2020;219:117026.

Download references

Acknowledgements

We would like to thank the professors from the Queensland University of Technology and the University of Information Technology and Communications who gave their feedback on the paper.

This research received no external funding.

Author information

Authors and affiliations.

School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia

Laith Alzubaidi & Jinglan Zhang

Control and Systems Engineering Department, University of Technology, Baghdad, 10001, Iraq

Amjad J. Humaidi

Electrical Engineering Technical College, Middle Technical University, Baghdad, 10001, Iraq

Ayad Al-Dujaili

Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO, 65211, USA

Ye Duan & Muthana Al-Amidie

AlNidhal Campus, University of Information Technology & Communications, Baghdad, 10001, Iraq

Laith Alzubaidi & Omran Al-Shamma

Department of Computer Science, University of Jaén, 23071, Jaén, Spain

J. Santamaría

College of Computer Science and Information Technology, University of Sumer, Thi Qar, 64005, Iraq

Mohammed A. Fadhel

School of Engineering, Manchester Metropolitan University, Manchester, M1 5GD, UK

Laith Farhan

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: LA, and JZ; methodology: LA, JZ, and JS; software: LA, and MAF; validation: LA, JZ, MA, and LF; formal analysis: LA, JZ, YD, and JS; investigation: LA, and JZ; resources: LA, JZ, and MAF; data curation: LA, and OA.; writing–original draft preparation: LA, and OA; writing—review and editing: LA, JZ, AJH, AA, YD, OA, JS, MAF, MA, and LF; visualization: LA, and MAF; supervision: JZ, and YD; project administration: JZ, YD, and JS; funding acquisition: LA, AJH, AA, and YD. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Laith Alzubaidi .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8 , 53 (2021). https://doi.org/10.1186/s40537-021-00444-8

Download citation

Received : 21 January 2021

Accepted : 22 March 2021

Published : 31 March 2021

DOI : https://doi.org/10.1186/s40537-021-00444-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Deep learning
  • Machine learning
  • Convolution neural network (CNN)
  • Deep neural network architectures
  • Deep learning applications
  • Image classification
  • Medical image analysis
  • Supervised learning

research papers on artificial neural networks pdf

Advertisement

Advertisement

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

  • Review Article
  • Published: 18 August 2021
  • Volume 2 , article number  420 , ( 2021 )

Cite this article

  • Iqbal H. Sarker   ORCID: orcid.org/0000-0003-1740-5517 1 , 2  

164k Accesses

541 Citations

17 Altmetric

Explore all metrics

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning , unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions . Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Similar content being viewed by others

research papers on artificial neural networks pdf

Machine Learning: Algorithms, Real-World Applications and Research Directions

Iqbal H. Sarker

research papers on artificial neural networks pdf

Machine learning and deep learning

Christian Janiesch, Patrick Zschech & Kai Heinrich

research papers on artificial neural networks pdf

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Vikas Hassija, Vinay Chamola, … Amir Hussain

Avoid common mistakes on your manuscript.

Introduction

In the late 1980s, neural networks became a prevalent topic in the area of Machine Learning (ML) as well as Artificial Intelligence (AI), due to the invention of various efficient learning methods and network structures [ 52 ]. Multilayer perceptron networks trained by “Backpropagation” type algorithms, self-organizing maps, and radial basis function networks were such innovative methods [ 26 , 36 , 37 ]. While neural networks are successfully used in many applications, the interest in researching this topic decreased later on. After that, in 2006, “Deep Learning” (DL) was introduced by Hinton et al. [ 41 ], which was based on the concept of artificial neural network (ANN). Deep learning became a prominent topic after that, resulting in a rebirth in neural network research, hence, some times referred to as “new-generation neural networks”. This is because deep networks, when properly trained, have produced significant success in a variety of classification and regression challenges [ 52 ].

Nowadays, DL technology is considered as one of the hot topics within the area of machine learning, artificial intelligence as well as data science and analytics, due to its learning capabilities from the given data. Many corporations including Google, Microsoft, Nokia, etc., study it actively as it can provide significant results in different classification and regression problems and datasets [ 52 ]. In terms of working domain, DL is considered as a subset of ML and AI, and thus DL can be seen as an AI function that mimics the human brain’s processing of data. The worldwide popularity of “Deep learning” is increasing day by day, which is shown in our earlier paper [ 96 ] based on the historical data collected from Google trends [ 33 ]. Deep learning differs from standard machine learning in terms of efficiency as the volume of data increases, discussed briefly in Section “ Why Deep Learning in Today's Research and Applications? ”. DL technology uses multiple layers to represent the abstractions of data to build computational models. While deep learning takes a long time to train a model due to a large number of parameters, it takes a short amount of time to run during testing as compared to other machine learning algorithms [ 127 ].

While today’s Fourth Industrial Revolution (4IR or Industry 4.0) is typically focusing on technology-driven “automation, smart and intelligent systems”, DL technology, which is originated from ANN, has become one of the core technologies to achieve the goal [ 103 , 114 ]. A typical neural network is mainly composed of many simple, connected processing elements or processors called neurons, each of which generates a series of real-valued activations for the target outcome. Figure 1 shows a schematic representation of the mathematical model of an artificial neuron, i.e., processing element, highlighting input ( \(X_i\) ), weight ( w ), bias ( b ), summation function ( \(\sum\) ), activation function ( f ) and corresponding output signal ( y ). Neural network-based DL technology is now widely applied in many fields and research areas such as healthcare, sentiment analysis, natural language processing, visual recognition, business intelligence, cybersecurity, and many more that have been summarized in the latter part of this paper.

figure 1

Schematic representation of the mathematical model of an artificial neuron (processing element), highlighting input ( \(X_i\) ), weight ( w ), bias ( b ), summation function ( \(\sum\) ), activation function ( f ) and output signal ( y )

Although DL models are successfully applied in various application areas, mentioned above, building an appropriate model of deep learning is a challenging task, due to the dynamic nature and variations of real-world problems and data. Moreover, DL models are typically considered as “black-box” machines that hamper the standard development of deep learning research and applications. Thus for clear understanding, in this paper, we present a structured and comprehensive view on DL techniques considering the variations in real-world problems and tasks. To achieve our goal, we briefly discuss various DL techniques and present a taxonomy by taking into account three major categories: (i) deep networks for supervised or discriminative learning that is utilized to provide a discriminative function in supervised deep learning or classification applications; (ii) deep networks for unsupervised or generative learning that are used to characterize the high-order correlation properties or features for pattern analysis or synthesis, thus can be used as preprocessing for the supervised algorithm; and (ii) deep networks for hybrid learning that is an integration of both supervised and unsupervised model and relevant others. We take into account such categories based on the nature and learning capabilities of different DL techniques and how they are used to solve problems in real-world applications [ 97 ]. Moreover, identifying key research issues and prospects including effective data representation, new algorithm design, data-driven hyper-parameter learning, and model optimization, integrating domain knowledge, adapting resource-constrained devices, etc. is one of the key targets of this study, which can lead to “Future Generation DL-Modeling”. Thus the goal of this paper is set to assist those in academia and industry as a reference guide, who want to research and develop data-driven smart and intelligent systems based on DL techniques.

The overall contribution of this paper is summarized as follows:

This article focuses on different aspects of deep learning modeling, i.e., the learning capabilities of DL techniques in different dimensions such as supervised or unsupervised tasks, to function in an automated and intelligent manner, which can play as a core technology of today’s Fourth Industrial Revolution (Industry 4.0).

We explore a variety of prominent DL techniques and present a taxonomy by taking into account the variations in deep learning tasks and how they are used for different purposes. In our taxonomy, we divide the techniques into three major categories such as deep networks for supervised or discriminative learning, unsupervised or generative learning, as well as deep networks for hybrid learning, and relevant others.

We have summarized several potential real-world application areas of deep learning, to assist developers as well as researchers in broadening their perspectives on DL techniques. Different categories of DL techniques highlighted in our taxonomy can be used to solve various issues accordingly.

Finally, we point out and discuss ten potential aspects with research directions for future generation DL modeling in terms of conducting future research and system development.

This paper is organized as follows. Section “ Why Deep Learning in Today's Research and Applications? ” motivates why deep learning is important to build data-driven intelligent systems. In Section“ Deep Learning Techniques and Applications ”, we present our DL taxonomy by taking into account the variations of deep learning tasks and how they are used in solving real-world issues and briefly discuss the techniques with summarizing the potential application areas. In Section “ Research Directions and Future Aspects ”, we discuss various research issues of deep learning-based modeling and highlight the promising topics for future research within the scope of our study. Finally, Section “ Concluding Remarks ” concludes this paper.

Why Deep Learning in Today’s Research and Applications?

The main focus of today’s Fourth Industrial Revolution (Industry 4.0) is typically technology-driven automation, smart and intelligent systems, in various application areas including smart healthcare, business intelligence, smart cities, cybersecurity intelligence, and many more [ 95 ]. Deep learning approaches have grown dramatically in terms of performance in a wide range of applications considering security technologies, particularly, as an excellent solution for uncovering complex architecture in high-dimensional data. Thus, DL techniques can play a key role in building intelligent data-driven systems according to today’s needs, because of their excellent learning capabilities from historical data. Consequently, DL can change the world as well as humans’ everyday life through its automation power and learning from experience. DL technology is therefore relevant to artificial intelligence [ 103 ], machine learning [ 97 ] and data science with advanced analytics [ 95 ] that are well-known areas in computer science, particularly, today’s intelligent computing. In the following, we first discuss regarding the position of deep learning in AI, or how DL technology is related to these areas of computing.

The Position of Deep Learning in AI

Nowadays, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three popular terms that are sometimes used interchangeably to describe systems or software that behaves intelligently. In Fig. 2 , we illustrate the position of deep Learning, comparing with machine learning and artificial intelligence. According to Fig. 2 , DL is a part of ML as well as a part of the broad area AI. In general, AI incorporates human behavior and intelligence to machines or systems [ 103 ], while ML is the method to learn from data or experience [ 97 ], which automates analytical model building. DL also represents learning methods from data where the computation is done through multi-layer neural networks and processing. The term “Deep” in the deep learning methodology refers to the concept of multiple levels or stages through which data is processed for building a data-driven model.

figure 2

An illustration of the position of deep learning (DL), comparing with machine learning (ML) and artificial intelligence (AI)

Thus, DL can be considered as one of the core technology of AI, a frontier for artificial intelligence, which can be used for building intelligent systems and automation. More importantly, it pushes AI to a new level, termed “Smarter AI”. As DL are capable of learning from data, there is a strong relation of deep learning with “Data Science” [ 95 ] as well. Typically, data science represents the entire process of finding meaning or insights in data in a particular problem domain, where DL methods can play a key role for advanced analytics and intelligent decision-making [ 104 , 106 ]. Overall, we can conclude that DL technology is capable to change the current world, particularly, in terms of a powerful computational engine and contribute to technology-driven automation, smart and intelligent systems accordingly, and meets the goal of Industry 4.0.

Understanding Various Forms of Data

As DL models learn from data, an in-depth understanding and representation of data are important to build a data-driven intelligent system in a particular application area. In the real world, data can be in various forms, which typically can be represented as below for deep learning modeling:

Sequential Data Sequential data is any kind of data where the order matters, i,e., a set of sequences. It needs to explicitly account for the sequential nature of input data while building the model. Text streams, audio fragments, video clips, time-series data, are some examples of sequential data.

Image or 2D Data A digital image is made up of a matrix, which is a rectangular array of numbers, symbols, or expressions arranged in rows and columns in a 2D array of numbers. Matrix, pixels, voxels, and bit depth are the four essential characteristics or fundamental parameters of a digital image.

Tabular Data A tabular dataset consists primarily of rows and columns. Thus tabular datasets contain data in a columnar format as in a database table. Each column (field) must have a name and each column may only contain data of the defined type. Overall, it is a logical and systematic arrangement of data in the form of rows and columns that are based on data properties or features. Deep learning models can learn efficiently on tabular data and allow us to build data-driven intelligent systems.

The above-discussed data forms are common in the real-world application areas of deep learning. Different categories of DL techniques perform differently depending on the nature and characteristics of data, discussed briefly in Section “ Deep Learning Techniques and Applications ” with a taxonomy presentation. However, in many real-world application areas, the standard machine learning techniques, particularly, logic-rule or tree-based techniques [ 93 , 101 ] perform significantly depending on the application nature. Figure 3 also shows the performance comparison of DL and ML modeling considering the amount of data. In the following, we highlight several cases, where deep learning is useful to solve real-world problems, according to our main focus in this paper.

DL Properties and Dependencies

A DL model typically follows the same processing stages as machine learning modeling. In Fig. 4 , we have shown a deep learning workflow to solve real-world problems, which consists of three processing steps, such as data understanding and preprocessing, DL model building, and training, and validation and interpretation. However, unlike the ML modeling [ 98 , 108 ], feature extraction in the DL model is automated rather than manual. K-nearest neighbor, support vector machines, decision tree, random forest, naive Bayes, linear regression, association rules, k-means clustering, are some examples of machine learning techniques that are commonly used in various application areas [ 97 ]. On the other hand, the DL model includes convolution neural network, recurrent neural network, autoencoder, deep belief network, and many more, discussed briefly with their potential application areas in Section 3 . In the following, we discuss the key properties and dependencies of DL techniques, that are needed to take into account before started working on DL modeling for real-world applications.

figure 3

An illustration of the performance comparison between deep learning (DL) and other machine learning (ML) algorithms, where DL modeling from large amounts of data can increase the performance

Data Dependencies Deep learning is typically dependent on a large amount of data to build a data-driven model for a particular problem domain. The reason is that when the data volume is small, deep learning algorithms often perform poorly [ 64 ]. In such circumstances, however, the performance of the standard machine-learning algorithms will be improved if the specified rules are used [ 64 , 107 ].

Hardware Dependencies The DL algorithms require large computational operations while training a model with large datasets. As the larger the computations, the more the advantage of a GPU over a CPU, the GPU is mostly used to optimize the operations efficiently. Thus, to work properly with the deep learning training, GPU hardware is necessary. Therefore, DL relies more on high-performance machines with GPUs than standard machine learning methods [ 19 , 127 ].

Feature Engineering Process Feature engineering is the process of extracting features (characteristics, properties, and attributes) from raw data using domain knowledge. A fundamental distinction between DL and other machine-learning techniques is the attempt to extract high-level characteristics directly from data [ 22 , 97 ]. Thus, DL decreases the time and effort required to construct a feature extractor for each problem.

Model Training and Execution time In general, training a deep learning algorithm takes a long time due to a large number of parameters in the DL algorithm; thus, the model training process takes longer. For instance, the DL models can take more than one week to complete a training session, whereas training with ML algorithms takes relatively little time, only seconds to hours [ 107 , 127 ]. During testing, deep learning algorithms take extremely little time to run [ 127 ], when compared to certain machine learning methods.

Black-box Perception and Interpretability Interpretability is an important factor when comparing DL with ML. It’s difficult to explain how a deep learning result was obtained, i.e., “black-box”. On the other hand, the machine-learning algorithms, particularly, rule-based machine learning techniques [ 97 ] provide explicit logic rules (IF-THEN) for making decisions that are easily interpretable for humans. For instance, in our earlier works, we have presented several machines learning rule-based techniques [ 100 , 102 , 105 ], where the extracted rules are human-understandable and easier to interpret, update or delete according to the target applications.

The most significant distinction between deep learning and regular machine learning is how well it performs when data grows exponentially. An illustration of the performance comparison between DL and standard ML algorithms has been shown in Fig. 3 , where DL modeling can increase the performance with the amount of data. Thus, DL modeling is extremely useful when dealing with a large amount of data because of its capacity to process vast amounts of features to build an effective data-driven model. In terms of developing and training DL models, it relies on parallelized matrix and tensor operations as well as computing gradients and optimization. Several, DL libraries and resources [ 30 ] such as PyTorch [ 82 ] (with a high-level API called Lightning) and TensorFlow [ 1 ] (which also offers Keras as a high-level API) offers these core utilities including many pre-trained models, as well as many other necessary functions for implementation and DL model building.

figure 4

A typical DL workflow to solve real-world problems, which consists of three sequential stages (i) data understanding and preprocessing (ii) DL model building and training (iii) validation and interpretation

Deep Learning Techniques and Applications

In this section, we go through the various types of deep neural network techniques, which typically consider several layers of information-processing stages in hierarchical structures to learn. A typical deep neural network contains multiple hidden layers including input and output layers. Figure 5 shows a general structure of a deep neural network ( \(hidden \; layer=N\) and N \(\ge\) 2) comparing with a shallow network ( \(hidden \; layer=1\) ). We also present our taxonomy on DL techniques based on how they are used to solve various problems, in this section. However, before exploring the details of the DL techniques, it’s useful to review various types of learning tasks such as (i) Supervised: a task-driven approach that uses labeled training data, (ii) Unsupervised: a data-driven process that analyzes unlabeled datasets, (iii) Semi-supervised: a hybridization of both the supervised and unsupervised methods, and (iv) Reinforcement: an environment driven approach, discussed briefly in our earlier paper [ 97 ]. Thus, to present our taxonomy, we divide DL techniques broadly into three major categories: (i) deep networks for supervised or discriminative learning; (ii) deep networks for unsupervised or generative learning; and (ii) deep networks for hybrid learning combing both and relevant others, as shown in Fig. 6 . In the following, we briefly discuss each of these techniques that can be used to solve real-world problems in various application areas according to their learning capabilities.

figure 5

A general architecture of a a shallow network with one hidden layer and b a deep neural network with multiple hidden layers

figure 6

A taxonomy of DL techniques, broadly divided into three major categories (i) deep networks for supervised or discriminative learning, (ii) deep networks for unsupervised or generative learning, and (ii) deep networks for hybrid learning and relevant others

Deep Networks for Supervised or Discriminative Learning

This category of DL techniques is utilized to provide a discriminative function in supervised or classification applications. Discriminative deep architectures are typically designed to give discriminative power for pattern classification by describing the posterior distributions of classes conditioned on visible data [ 21 ]. Discriminative architectures mainly include Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN or ConvNet), Recurrent Neural Networks (RNN), along with their variants. In the following, we briefly discuss these techniques.

Multi-layer Perceptron (MLP)

Multi-layer Perceptron (MLP), a supervised learning approach [ 83 ], is a type of feedforward artificial neural network (ANN). It is also known as the foundation architecture of deep neural networks (DNN) or deep learning. A typical MLP is a fully connected network that consists of an input layer that receives input data, an output layer that makes a decision or prediction about the input signal, and one or more hidden layers between these two that are considered as the network’s computational engine [ 36 , 103 ]. The output of an MLP network is determined using a variety of activation functions, also known as transfer functions, such as ReLU (Rectified Linear Unit), Tanh, Sigmoid, and Softmax [ 83 , 96 ]. To train MLP employs the most extensively used algorithm “Backpropagation” [ 36 ], a supervised learning technique, which is also known as the most basic building block of a neural network. During the training process, various optimization approaches such as Stochastic Gradient Descent (SGD), Limited Memory BFGS (L-BFGS), and Adaptive Moment Estimation (Adam) are applied. MLP requires tuning of several hyperparameters such as the number of hidden layers, neurons, and iterations, which could make solving a complicated model computationally expensive. However, through partial fit, MLP offers the advantage of learning non-linear models in real-time or online [ 83 ].

Convolutional Neural Network (CNN or ConvNet)

The Convolutional Neural Network (CNN or ConvNet) [ 65 ] is a popular discriminative deep learning architecture that learns directly from the input without the need for human feature extraction. Figure 7 shows an example of a CNN including multiple convolutions and pooling layers. As a result, the CNN enhances the design of traditional ANN like regularized MLP networks. Each layer in CNN takes into account optimum parameters for a meaningful output as well as reduces model complexity. CNN also uses a ‘dropout’ [ 30 ] that can deal with the problem of over-fitting, which may occur in a traditional network.

figure 7

An example of a convolutional neural network (CNN or ConvNet) including multiple convolution and pooling layers

CNNs are specifically intended to deal with a variety of 2D shapes and are thus widely employed in visual recognition, medical image analysis, image segmentation, natural language processing, and many more [ 65 , 96 ]. The capability of automatically discovering essential features from the input without the need for human intervention makes it more powerful than a traditional network. Several variants of CNN are exist in the area that includes visual geometry group (VGG) [ 38 ], AlexNet [ 62 ], Xception [ 17 ], Inception [ 116 ], ResNet [ 39 ], etc. that can be used in various application domains according to their learning capabilities.

Recurrent Neural Network (RNN) and its Variants

A Recurrent Neural Network (RNN) is another popular neural network, which employs sequential or time-series data and feeds the output from the previous step as input to the current stage [ 27 , 74 ]. Like feedforward and CNN, recurrent networks learn from training input, however, distinguish by their “memory”, which allows them to impact current input and output through using information from previous inputs. Unlike typical DNN, which assumes that inputs and outputs are independent of one another, the output of RNN is reliant on prior elements within the sequence. However, standard recurrent networks have the issue of vanishing gradients, which makes learning long data sequences challenging. In the following, we discuss several popular variants of the recurrent network that minimizes the issues and perform well in many real-world application domains.

Long short-term memory (LSTM) This is a popular form of RNN architecture that uses special units to deal with the vanishing gradient problem, which was introduced by Hochreiter et al. [ 42 ]. A memory cell in an LSTM unit can store data for long periods and the flow of information into and out of the cell is managed by three gates. For instance, the ‘Forget Gate’ determines what information from the previous state cell will be memorized and what information will be removed that is no longer useful, while the ‘Input Gate’ determines which information should enter the cell state and the ‘Output Gate’ determines and controls the outputs. As it solves the issues of training a recurrent network, the LSTM network is considered one of the most successful RNN.

Bidirectional RNN/LSTM Bidirectional RNNs connect two hidden layers that run in opposite directions to a single output, allowing them to accept data from both the past and future. Bidirectional RNNs, unlike traditional recurrent networks, are trained to predict both positive and negative time directions at the same time. A Bidirectional LSTM, often known as a BiLSTM, is an extension of the standard LSTM that can increase model performance on sequence classification issues [ 113 ]. It is a sequence processing model comprising of two LSTMs: one takes the input forward and the other takes it backward. Bidirectional LSTM in particular is a popular choice in natural language processing tasks.

Gated recurrent units (GRUs) A Gated Recurrent Unit (GRU) is another popular variant of the recurrent network that uses gating methods to control and manage information flow between cells in the neural network, introduced by Cho et al. [ 16 ]. The GRU is like an LSTM, however, has fewer parameters, as it has a reset gate and an update gate but lacks the output gate, as shown in Fig. 8 . Thus, the key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). The GRU’s structure enables it to capture dependencies from large sequences of data in an adaptive manner, without discarding information from earlier parts of the sequence. Thus GRU is a slightly more streamlined variant that often offers comparable performance and is significantly faster to compute [ 18 ]. Although GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets [ 18 , 34 ], both variants of RNN have proven their effectiveness while producing the outcome.

figure 8

Basic structure of a gated recurrent unit (GRU) cell consisting of reset and update gates

Overall, the basic property of a recurrent network is that it has at least one feedback connection, which enables activations to loop. This allows the networks to do temporal processing and sequence learning, such as sequence recognition or reproduction, temporal association or prediction, etc. Following are some popular application areas of recurrent networks such as prediction problems, machine translation, natural language processing, text summarization, speech recognition, and many more.

Deep Networks for Generative or Unsupervised Learning

This category of DL techniques is typically used to characterize the high-order correlation properties or features for pattern analysis or synthesis, as well as the joint statistical distributions of the visible data and their associated classes [ 21 ]. The key idea of generative deep architectures is that during the learning process, precise supervisory information such as target class labels is not of concern. As a result, the methods under this category are essentially applied for unsupervised learning as the methods are typically used for feature learning or data generating and representation [ 20 , 21 ]. Thus generative modeling can be used as preprocessing for the supervised learning tasks as well, which ensures the discriminative model accuracy. Commonly used deep neural network techniques for unsupervised or generative learning are Generative Adversarial Network (GAN), Autoencoder (AE), Restricted Boltzmann Machine (RBM), Self-Organizing Map (SOM), and Deep Belief Network (DBN) along with their variants.

Generative Adversarial Network (GAN)

A Generative Adversarial Network (GAN), designed by Ian Goodfellow [ 32 ], is a type of neural network architecture for generative modeling to create new plausible samples on demand. It involves automatically discovering and learning regularities or patterns in input data so that the model may be used to generate or output new examples from the original dataset. As shown in Fig. 9 , GANs are composed of two neural networks, a generator G that creates new data having properties similar to the original data, and a discriminator D that predicts the likelihood of a subsequent sample being drawn from actual data rather than data provided by the generator. Thus in GAN modeling, both the generator and discriminator are trained to compete with each other. While the generator tries to fool and confuse the discriminator by creating more realistic data, the discriminator tries to distinguish the genuine data from the fake data generated by G .

figure 9

Schematic structure of a standard generative adversarial network (GAN)

Generally, GAN network deployment is designed for unsupervised learning tasks, but it has also proven to be a better solution for semi-supervised and reinforcement learning as well depending on the task [ 3 ]. GANs are also used in state-of-the-art transfer learning research to enforce the alignment of the latent feature space [ 66 ]. Inverse models, such as Bidirectional GAN (BiGAN) [ 25 ] can also learn a mapping from data to the latent space, similar to how the standard GAN model learns a mapping from a latent space to the data distribution. The potential application areas of GAN networks are healthcare, image analysis, data augmentation, video generation, voice generation, pandemics, traffic control, cybersecurity, and many more, which are increasing rapidly. Overall, GANs have established themselves as a comprehensive domain of independent data expansion and as a solution to problems requiring a generative solution.

Auto-Encoder (AE) and Its Variants

An auto-encoder (AE) [ 31 ] is a popular unsupervised learning technique in which neural networks are used to learn representations. Typically, auto-encoders are used to work with high-dimensional data, and dimensionality reduction explains how a set of data is represented. Encoder, code, and decoder are the three parts of an autoencoder. The encoder compresses the input and generates the code, which the decoder subsequently uses to reconstruct the input. The AEs have recently been used to learn generative data models [ 69 ]. The auto-encoder is widely used in many unsupervised learning tasks, e.g., dimensionality reduction, feature extraction, efficient coding, generative modeling, denoising, anomaly or outlier detection, etc. [ 31 , 132 ]. Principal component analysis (PCA) [ 99 ], which is also used to reduce the dimensionality of huge data sets, is essentially similar to a single-layered AE with a linear activation function. Regularized autoencoders such as sparse, denoising, and contractive are useful for learning representations for later classification tasks [ 119 ], while variational autoencoders can be used as generative models [ 56 ], discussed below.

Sparse Autoencoder (SAE) A sparse autoencoder [ 73 ] has a sparsity penalty on the coding layer as a part of its training requirement. SAEs may have more hidden units than inputs, but only a small number of hidden units are permitted to be active at the same time, resulting in a sparse model. Figure 10 shows a schematic structure of a sparse autoencoder with several active units in the hidden layer. This model is thus obliged to respond to the unique statistical features of the training data following its constraints.

Denoising Autoencoder (DAE) A denoising autoencoder is a variant on the basic autoencoder that attempts to improve representation (to extract useful features) by altering the reconstruction criterion, and thus reduces the risk of learning the identity function [ 31 , 119 ]. In other words, it receives a corrupted data point as input and is trained to recover the original undistorted input as its output through minimizing the average reconstruction error over the training data, i.e, cleaning the corrupted input, or denoising. Thus, in the context of computing, DAEs can be considered as very powerful filters that can be utilized for automatic pre-processing. A denoising autoencoder, for example, could be used to automatically pre-process an image, thereby boosting its quality for recognition accuracy.

Contractive Autoencoder (CAE) The idea behind a contractive autoencoder, proposed by Rifai et al. [ 90 ], is to make the autoencoders robust of small changes in the training dataset. In its objective function, a CAE includes an explicit regularizer that forces the model to learn an encoding that is robust to small changes in input values. As a result, the learned representation’s sensitivity to the training input is reduced. While DAEs encourage the robustness of reconstruction as discussed above, CAEs encourage the robustness of representation.

Variational Autoencoder (VAE) A variational autoencoder [ 55 ] has a fundamentally unique property that distinguishes it from the classical autoencoder discussed above, which makes this so effective for generative modeling. VAEs, unlike the traditional autoencoders which map the input onto a latent vector, map the input data into the parameters of a probability distribution, such as the mean and variance of a Gaussian distribution. A VAE assumes that the source data has an underlying probability distribution and then tries to discover the distribution’s parameters. Although this approach was initially designed for unsupervised learning, its use has been demonstrated in other domains such as semi-supervised learning [ 128 ] and supervised learning [ 51 ].

figure 10

Schematic structure of a sparse autoencoder (SAE) with several active units (filled circle) in the hidden layer

Although, the earlier concept of AE was typically for dimensionality reduction or feature learning mentioned above, recently, AEs have been brought to the forefront of generative modeling, even the generative adversarial network is one of the popular methods in the area. The AEs have been effectively employed in a variety of domains, including healthcare, computer vision, speech recognition, cybersecurity, natural language processing, and many more. Overall, we can conclude that auto-encoder and its variants can play a significant role as unsupervised feature learning with neural network architecture.

Kohonen Map or Self-Organizing Map (SOM)

A Self-Organizing Map (SOM) or Kohonen Map [ 59 ] is another form of unsupervised learning technique for creating a low-dimensional (usually two-dimensional) representation of a higher-dimensional data set while maintaining the topological structure of the data. SOM is also known as a neural network-based dimensionality reduction algorithm that is commonly used for clustering [ 118 ]. A SOM adapts to the topological form of a dataset by repeatedly moving its neurons closer to the data points, allowing us to visualize enormous datasets and find probable clusters. The first layer of a SOM is the input layer, and the second layer is the output layer or feature map. Unlike other neural networks that use error-correction learning, such as backpropagation with gradient descent [ 36 ], SOMs employ competitive learning, which uses a neighborhood function to retain the input space’s topological features. SOM is widely utilized in a variety of applications, including pattern identification, health or medical diagnosis, anomaly detection, and virus or worm attack detection [ 60 , 87 ]. The primary benefit of employing a SOM is that this can make high-dimensional data easier to visualize and analyze to understand the patterns. The reduction of dimensionality and grid clustering makes it easy to observe similarities in the data. As a result, SOMs can play a vital role in developing a data-driven effective model for a particular problem domain, depending on the data characteristics.

Restricted Boltzmann Machine (RBM)

A Restricted Boltzmann Machine (RBM) [ 75 ] is also a generative stochastic neural network capable of learning a probability distribution across its inputs. Boltzmann machines typically consist of visible and hidden nodes and each node is connected to every other node, which helps us understand irregularities by learning how the system works in normal circumstances. RBMs are a subset of Boltzmann machines that have a limit on the number of connections between the visible and hidden layers [ 77 ]. This restriction permits training algorithms like the gradient-based contrastive divergence algorithm to be more efficient than those for Boltzmann machines in general [ 41 ]. RBMs have found applications in dimensionality reduction, classification, regression, collaborative filtering, feature learning, topic modeling, and many others. In the area of deep learning modeling, they can be trained either supervised or unsupervised, depending on the task. Overall, the RBMs can recognize patterns in data automatically and develop probabilistic or stochastic models, which are utilized for feature selection or extraction, as well as forming a deep belief network.

Deep Belief Network (DBN)

A Deep Belief Network (DBN) [ 40 ] is a multi-layer generative graphical model of stacking several individual unsupervised networks such as AEs or RBMs, that use each network’s hidden layer as the input for the next layer, i.e, connected sequentially. Thus, we can divide a DBN into (i) AE-DBN which is known as stacked AE, and (ii) RBM-DBN that is known as stacked RBM, where AE-DBN is composed of autoencoders and RBM-DBN is composed of restricted Boltzmann machines, discussed earlier. The ultimate goal is to develop a faster-unsupervised training technique for each sub-network that depends on contrastive divergence [ 41 ]. DBN can capture a hierarchical representation of input data based on its deep structure. The primary idea behind DBN is to train unsupervised feed-forward neural networks with unlabeled data before fine-tuning the network with labeled input. One of the most important advantages of DBN, as opposed to typical shallow learning networks, is that it permits the detection of deep patterns, which allows for reasoning abilities and the capture of the deep difference between normal and erroneous data [ 89 ]. A continuous DBN is simply an extension of a standard DBN that allows a continuous range of decimals instead of binary data. Overall, the DBN model can play a key role in a wide range of high-dimensional data applications due to its strong feature extraction and classification capabilities and become one of the significant topics in the field of neural networks.

In summary, the generative learning techniques discussed above typically allow us to generate a new representation of data through exploratory analysis. As a result, these deep generative networks can be utilized as preprocessing for supervised or discriminative learning tasks, as well as ensuring model accuracy, where unsupervised representation learning can allow for improved classifier generalization.

Deep Networks for Hybrid Learning and Other Approaches

In addition to the above-discussed deep learning categories, hybrid deep networks and several other approaches such as deep transfer learning (DTL) and deep reinforcement learning (DRL) are popular, which are discussed in the following.

Hybrid Deep Neural Networks

Generative models are adaptable, with the capacity to learn from both labeled and unlabeled data. Discriminative models, on the other hand, are unable to learn from unlabeled data yet outperform their generative counterparts in supervised tasks. A framework for training both deep generative and discriminative models simultaneously can enjoy the benefits of both models, which motivates hybrid networks.

Hybrid deep learning models are typically composed of multiple (two or more) deep basic learning models, where the basic model is a discriminative or generative deep learning model discussed earlier. Based on the integration of different basic generative or discriminative models, the below three categories of hybrid deep learning models might be useful for solving real-world problems. These are as follows:

Hybrid \(Model\_1\) : An integration of different generative or discriminative models to extract more meaningful and robust features. Examples could be CNN+LSTM, AE+GAN, and so on.

Hybrid \(Model\_2\) : An integration of generative model followed by a discriminative model. Examples could be DBN+MLP, GAN+CNN, AE+CNN, and so on.

Hybrid \(Model\_3\) : An integration of generative or discriminative model followed by a non-deep learning classifier. Examples could be AE+SVM, CNN+SVM, and so on.

Thus, in a broad sense, we can conclude that hybrid models can be either classification-focused or non-classification depending on the target use. However, most of the hybrid learning-related studies in the area of deep learning are classification-focused or supervised learning tasks, summarized in Table 1 . The unsupervised generative models with meaningful representations are employed to enhance the discriminative models. The generative models with useful representation can provide more informative and low-dimensional features for discrimination, and they can also enable to enhance the training data quality and quantity, providing additional information for classification.

Deep Transfer Learning (DTL)

Transfer Learning is a technique for effectively using previously learned model knowledge to solve a new task with minimum training or fine-tuning. In comparison to typical machine learning techniques [ 97 ], DL takes a large amount of training data. As a result, the need for a substantial volume of labeled data is a significant barrier to address some essential domain-specific tasks, particularly, in the medical sector, where creating large-scale, high-quality annotated medical or health datasets is both difficult and costly. Furthermore, the standard DL model demands a lot of computational resources, such as a GPU-enabled server, even though researchers are working hard to improve it. As a result, Deep Transfer Learning (DTL), a DL-based transfer learning method, might be helpful to address this issue. Figure 11 shows a general structure of the transfer learning process, where knowledge from the pre-trained model is transferred into a new DL model. It’s especially popular in deep learning right now since it allows to train deep neural networks with very little data [ 126 ].

figure 11

A general structure of transfer learning process, where knowledge from pre-trained model is transferred into new DL model

Transfer learning is a two-stage approach for training a DL model that consists of a pre-training step and a fine-tuning step in which the model is trained on the target task. Since deep neural networks have gained popularity in a variety of fields, a large number of DTL methods have been presented, making it crucial to categorize and summarize them. Based on the techniques used in the literature, DTL can be classified into four categories [ 117 ]. These are (i) instances-based deep transfer learning that utilizes instances in source domain by appropriate weight, (ii) mapping-based deep transfer learning that maps instances from two domains into a new data space with better similarity, (iii) network-based deep transfer learning that reuses the partial of network pre-trained in the source domain, and (iv) adversarial based deep transfer learning that uses adversarial technology to find transferable features that both suitable for two domains. Due to its high effectiveness and practicality, adversarial-based deep transfer learning has exploded in popularity in recent years. Transfer learning can also be classified into inductive, transductive, and unsupervised transfer learning depending on the circumstances between the source and target domains and activities [ 81 ]. While most current research focuses on supervised learning, how deep neural networks can transfer knowledge in unsupervised or semi-supervised learning may gain further interest in the future. DTL techniques are useful in a variety of fields including natural language processing, sentiment classification, visual recognition, speech recognition, spam filtering, and relevant others.

Deep Reinforcement Learning (DRL)

Reinforcement learning takes a different approach to solving the sequential decision-making problem than other approaches we have discussed so far. The concepts of an environment and an agent are often introduced first in reinforcement learning. The agent can perform a series of actions in the environment, each of which has an impact on the environment’s state and can result in possible rewards (feedback) - “positive” for good sequences of actions that result in a “good” state, and “negative” for bad sequences of actions that result in a “bad” state. The purpose of reinforcement learning is to learn good action sequences through interaction with the environment, typically referred to as a policy.

figure 12

Schematic structure of deep reinforcement learning (DRL) highlighting a deep neural network

Deep reinforcement learning (DRL or deep RL) [ 9 ] integrates neural networks with a reinforcement learning architecture to allow the agents to learn the appropriate actions in a virtual environment, as shown in Fig. 12 . In the area of reinforcement learning, model-based RL is based on learning a transition model that enables for modeling of the environment without interacting with it directly, whereas model-free RL methods learn directly from interactions with the environment. Q-learning is a popular model-free RL technique for determining the best action-selection policy for any (finite) Markov Decision Process (MDP) [ 86 , 97 ]. MDP is a mathematical framework for modeling decisions based on state, action, and rewards [ 86 ]. In addition, Deep Q-Networks, Double DQN, Bi-directional Learning, Monte Carlo Control, etc. are used in the area [ 50 , 97 ]. In DRL methods it incorporates DL models, e.g. Deep Neural Networks (DNN), based on MDP principle [ 71 ], as policy and/or value function approximators. CNN for example can be used as a component of RL agents to learn directly from raw, high-dimensional visual inputs. In the real world, DRL-based solutions can be used in several application areas including robotics, video games, natural language processing, computer vision, and relevant others.

figure 13

Several potential real-world application areas of deep learning

Deep Learning Application Summary

During the past few years, deep learning has been successfully applied to numerous problems in many application areas. These include natural language processing, sentiment analysis, cybersecurity, business, virtual assistants, visual recognition, healthcare, robotics, and many more. In Fig. 13 , we have summarized several potential real-world application areas of deep learning. Various deep learning techniques according to our presented taxonomy in Fig. 6 that includes discriminative learning, generative learning, as well as hybrid models, discussed earlier, are employed in these application areas. In Table 1 , we have also summarized various deep learning tasks and techniques that are used to solve the relevant tasks in several real-world applications areas. Overall, from Fig. 13 and Table 1 , we can conclude that the future prospects of deep learning modeling in real-world application areas are huge and there are lots of scopes to work. In the next section, we also summarize the research issues in deep learning modeling and point out the potential aspects for future generation DL modeling.

Research Directions and Future Aspects

While existing methods have established a solid foundation for deep learning systems and research, this section outlines the below ten potential future research directions based on our study.

Automation in Data Annotation According to the existing literature, discussed in Section 3 , most of the deep learning models are trained through publicly available datasets that are annotated. However, to build a system for a new problem domain or recent data-driven system, raw data from relevant sources are needed to collect. Thus, data annotation, e.g., categorization, tagging, or labeling of a large amount of raw data, is important for building discriminative deep learning models or supervised tasks, which is challenging. A technique with the capability of automatic and dynamic data annotation, rather than manual annotation or hiring annotators, particularly, for large datasets, could be more effective for supervised learning as well as minimizing human effort. Therefore, a more in-depth investigation of data collection and annotation methods, or designing an unsupervised learning-based solution could be one of the primary research directions in the area of deep learning modeling.

Data Preparation for Ensuring Data Quality As discussed earlier throughout the paper, the deep learning algorithms highly impact data quality, and availability for training, and consequently on the resultant model for a particular problem domain. Thus, deep learning models may become worthless or yield decreased accuracy if the data is bad, such as data sparsity, non-representative, poor-quality, ambiguous values, noise, data imbalance, irrelevant features, data inconsistency, insufficient quantity, and so on for training. Consequently, such issues in data can lead to poor processing and inaccurate findings, which is a major problem while discovering insights from data. Thus deep learning models also need to adapt to such rising issues in data, to capture approximated information from observations. Therefore, effective data pre-processing techniques are needed to design according to the nature of the data problem and characteristics, to handling such emerging challenges, which could be another research direction in the area.

Black-box Perception and Proper DL/ML Algorithm Selection In general, it’s difficult to explain how a deep learning result is obtained or how they get the ultimate decisions for a particular model. Although DL models achieve significant performance while learning from large datasets, as discussed in Section 2 , this “black-box” perception of DL modeling typically represents weak statistical interpretability that could be a major issue in the area. On the other hand, ML algorithms, particularly, rule-based machine learning techniques provide explicit logic rules (IF-THEN) for making decisions that are easier to interpret, update or delete according to the target applications [ 97 , 100 , 105 ]. If the wrong learning algorithm is chosen, unanticipated results may occur, resulting in a loss of effort as well as the model’s efficacy and accuracy. Thus by taking into account the performance, complexity, model accuracy, and applicability, selecting an appropriate model for the target application is challenging, and in-depth analysis is needed for better understanding and decision making.

Deep Networks for Supervised or Discriminative Learning: According to our designed taxonomy of deep learning techniques, as shown in Fig. 6 , discriminative architectures mainly include MLP, CNN, and RNN, along with their variants that are applied widely in various application domains. However, designing new techniques or their variants of such discriminative techniques by taking into account model optimization, accuracy, and applicability, according to the target real-world application and the nature of the data, could be a novel contribution, which can also be considered as a major future aspect in the area of supervised or discriminative learning.

Deep Networks for Unsupervised or Generative Learning As discussed in Section 3 , unsupervised learning or generative deep learning modeling is one of the major tasks in the area, as it allows us to characterize the high-order correlation properties or features in data, or generating a new representation of data through exploratory analysis. Moreover, unlike supervised learning [ 97 ], it does not require labeled data due to its capability to derive insights directly from the data as well as data-driven decision making. Consequently, it thus can be used as preprocessing for supervised learning or discriminative modeling as well as semi-supervised learning tasks, which ensure learning accuracy and model efficiency. According to our designed taxonomy of deep learning techniques, as shown in Fig. 6 , generative techniques mainly include GAN, AE, SOM, RBM, DBN, and their variants. Thus, designing new techniques or their variants for an effective data modeling or representation according to the target real-world application could be a novel contribution, which can also be considered as a major future aspect in the area of unsupervised or generative learning.

Hybrid/Ensemble Modeling and Uncertainty Handling According to our designed taxonomy of DL techniques, as shown in Fig 6 , this is considered as another major category in deep learning tasks. As hybrid modeling enjoys the benefits of both generative and discriminative learning, an effective hybridization can outperform others in terms of performance as well as uncertainty handling in high-risk applications. In Section 3 , we have summarized various types of hybridization, e.g., AE+CNN/SVM. Since a group of neural networks is trained with distinct parameters or with separate sub-sampling training datasets, hybridization or ensembles of such techniques, i.e., DL with DL/ML, can play a key role in the area. Thus designing effective blended discriminative and generative models accordingly rather than naive method, could be an important research opportunity to solve various real-world issues including semi-supervised learning tasks and model uncertainty.

Dynamism in Selecting Threshold/ Hyper-parameters Values, and Network Structures with Computational Efficiency In general, the relationship among performance, model complexity, and computational requirements is a key issue in deep learning modeling and applications. A combination of algorithmic advancements with improved accuracy as well as maintaining computational efficiency, i.e., achieving the maximum throughput while consuming the least amount of resources, without significant information loss, can lead to a breakthrough in the effectiveness of deep learning modeling in future real-world applications. The concept of incremental approaches or recency-based learning [ 100 ] might be effective in several cases depending on the nature of target applications. Moreover, assuming the network structures with a static number of nodes and layers, hyper-parameters values or threshold settings, or selecting them by the trial-and-error process may not be effective in many cases, as it can be changed due to the changes in data. Thus, a data-driven approach to select them dynamically could be more effective while building a deep learning model in terms of both performance and real-world applicability. Such type of data-driven automation can lead to future generation deep learning modeling with additional intelligence, which could be a significant future aspect in the area as well as an important research direction to contribute.

Lightweight Deep Learning Modeling for Next-Generation Smart Devices and Applications: In recent years, the Internet of Things (IoT) consisting of billions of intelligent and communicating things and mobile communications technologies have become popular to detect and gather human and environmental information (e.g. geo-information, weather data, bio-data, human behaviors, and so on) for a variety of intelligent services and applications. Every day, these ubiquitous smart things or devices generate large amounts of data, requiring rapid data processing on a variety of smart mobile devices [ 72 ]. Deep learning technologies can be incorporate to discover underlying properties and to effectively handle such large amounts of sensor data for a variety of IoT applications including health monitoring and disease analysis, smart cities, traffic flow prediction, and monitoring, smart transportation, manufacture inspection, fault assessment, smart industry or Industry 4.0, and many more. Although deep learning techniques discussed in Section 3 are considered as powerful tools for processing big data, lightweight modeling is important for resource-constrained devices, due to their high computational cost and considerable memory overhead. Thus several techniques such as optimization, simplification, compression, pruning, generalization, important feature extraction, etc. might be helpful in several cases. Therefore, constructing the lightweight deep learning techniques based on a baseline network architecture to adapt the DL model for next-generation mobile, IoT, or resource-constrained devices and applications, could be considered as a significant future aspect in the area.

Incorporating Domain Knowledge into Deep Learning Modeling Domain knowledge, as opposed to general knowledge or domain-independent knowledge, is knowledge of a specific, specialized topic or field. For instance, in terms of natural language processing, the properties of the English language typically differ from other languages like Bengali, Arabic, French, etc. Thus integrating domain-based constraints into the deep learning model could produce better results for such particular purpose. For instance, a task-specific feature extractor considering domain knowledge in smart manufacturing for fault diagnosis can resolve the issues in traditional deep-learning-based methods [ 28 ]. Similarly, domain knowledge in medical image analysis [ 58 ], financial sentiment analysis [ 49 ], cybersecurity analytics [ 94 , 103 ] as well as conceptual data model in which semantic information, (i.e., meaningful for a system, rather than merely correlational) [ 45 , 121 , 131 ] is included, can play a vital role in the area. Transfer learning could be an effective way to get started on a new challenge with domain knowledge. Moreover, contextual information such as spatial, temporal, social, environmental contexts [ 92 , 104 , 108 ] can also play an important role to incorporate context-aware computing with domain knowledge for smart decision making as well as building adaptive and intelligent context-aware systems. Therefore understanding domain knowledge and effectively incorporating them into the deep learning model could be another research direction.

Designing General Deep Learning Framework for Target Application Domains One promising research direction for deep learning-based solutions is to develop a general framework that can handle data diversity, dimensions, stimulation types, etc. The general framework would require two key capabilities: the attention mechanism that focuses on the most valuable parts of input signals, and the ability to capture latent feature that enables the framework to capture the distinctive and informative features. Attention models have been a popular research topic because of their intuition, versatility, and interpretability, and employed in various application areas like computer vision, natural language processing, text or image classification, sentiment analysis, recommender systems, user profiling, etc [ 13 , 80 ]. Attention mechanism can be implemented based on learning algorithms such as reinforcement learning that is capable of finding the most useful part through a policy search [ 133 , 134 ]. Similarly, CNN can be integrated with suitable attention mechanisms to form a general classification framework, where CNN can be used as a feature learning tool for capturing features in various levels and ranges. Thus, designing a general deep learning framework considering attention as well as a latent feature for target application domains could be another area to contribute.

To summarize, deep learning is a fairly open topic to which academics can contribute by developing new methods or improving existing methods to handle the above-mentioned concerns and tackle real-world problems in a variety of application areas. This can also help the researchers conduct a thorough analysis of the application’s hidden and unexpected challenges to produce more reliable and realistic outcomes. Overall, we can conclude that addressing the above-mentioned issues and contributing to proposing effective and efficient techniques could lead to “Future Generation DL” modeling as well as more intelligent and automated applications.

Concluding Remarks

In this article, we have presented a structured and comprehensive view of deep learning technology, which is considered a core part of artificial intelligence as well as data science. It starts with a history of artificial neural networks and moves to recent deep learning techniques and breakthroughs in different applications. Then, the key algorithms in this area, as well as deep neural network modeling in various dimensions are explored. For this, we have also presented a taxonomy considering the variations of deep learning tasks and how they are used for different purposes. In our comprehensive study, we have taken into account not only the deep networks for supervised or discriminative learning but also the deep networks for unsupervised or generative learning, and hybrid learning that can be used to solve a variety of real-world issues according to the nature of problems.

Deep learning, unlike traditional machine learning and data mining algorithms, can produce extremely high-level data representations from enormous amounts of raw data. As a result, it has provided an excellent solution to a variety of real-world problems. A successful deep learning technique must possess the relevant data-driven modeling depending on the characteristics of raw data. The sophisticated learning algorithms then need to be trained through the collected data and knowledge related to the target application before the system can assist with intelligent decision-making. Deep learning has shown to be useful in a wide range of applications and research areas such as healthcare, sentiment analysis, visual recognition, business intelligence, cybersecurity, and many more that are summarized in the paper.

Finally, we have summarized and discussed the challenges faced and the potential research directions, and future aspects in the area. Although deep learning is considered a black-box solution for many applications due to its poor reasoning and interpretability, addressing the challenges or future aspects that are identified could lead to future generation deep learning modeling and smarter systems. This can also help the researchers for in-depth analysis to produce more reliable and realistic outcomes. Overall, we believe that our study on neural networks and deep learning-based advanced analytics points in a promising path and can be utilized as a reference guide for future research and implementations in relevant application domains by both academic and industry professionals.

Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin Ma, Ghemawat S, Irving G, Isard M, et al. Tensorflow: a system for large-scale machine learning. In: 12th { USENIX } Symposium on operating systems design and implementation ({ OSDI } 16), 2016; p. 265–283.

Abdel-Basset M, Hawash H, Chakrabortty RK, Ryan M. Energy-net: a deep learning approach for smart energy management in iot-based smart cities. IEEE Internet of Things J. 2021.

Aggarwal A, Mittal M, Battineni G. Generative adversarial network: an overview of theory and applications. Int J Inf Manag Data Insights. 2021; p. 100004.

Al-Qatf M, Lasheng Y, Al-Habib M, Al-Sabahi K. Deep learning approach combining sparse autoencoder with svm for network intrusion detection. IEEE Access. 2018;6:52843–56.

Article   Google Scholar  

Ale L, Sheta A, Li L, Wang Y, Zhang N. Deep learning based plant disease detection for smart agriculture. In: 2019 IEEE Globecom Workshops (GC Wkshps), 2019; p. 1–6. IEEE.

Amarbayasgalan T, Lee JY, Kim KR, Ryu KH. Deep autoencoder based neural networks for coronary heart disease risk prediction. In: Heterogeneous data management, polystores, and analytics for healthcare. Springer; 2019. p. 237–48.

Anuradha J, et al. Big data based stock trend prediction using deep cnn with reinforcement-lstm model. Int J Syst Assur Eng Manag. 2021; p. 1–11.

Aqib M, Mehmood R, Albeshri A, Alzahrani A. Disaster management in smart cities by forecasting traffic plan using deep learning and gpus. In: International Conference on smart cities, infrastructure, technologies and applications. Springer; 2017. p. 139–54.

Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA. Deep reinforcement learning: a brief survey. IEEE Signal Process Mag. 2017;34(6):26–38.

Aslan MF, Unlersen MF, Sabanci K, Durdu A. Cnn-based transfer learning-bilstm network: a novel approach for covid-19 infection detection. Appl Soft Comput. 2021;98:106912.

Bu F, Wang X. A smart agriculture iot system based on deep reinforcement learning. Futur Gener Comput Syst. 2019;99:500–7.

Chang W-J, Chen L-B, Hsu C-H, Lin C-P, Yang T-C. A deep learning-based intelligent medicine recognition system for chronic patients. IEEE Access. 2019;7:44441–58.

Chaudhari S, Mithal V, Polatkan Gu, Ramanath R. An attentive survey of attention models. arXiv preprint arXiv:1904.02874, 2019.

Chaudhuri N, Gupta G, Vamsi V, Bose I. On the platform but will they buy? predicting customers’ purchase behavior using deep learning. Decis Support Syst. 2021; p. 113622.

Chen D, Wawrzynski P, Lv Z. Cyber security in smart cities: a review of deep learning-based applications and case studies. Sustain Cities Soc. 2020; p. 102655.

Cho K, Van MB, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.

Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2017; p. 1251–258.

Chung J, Gulcehre C, Cho KH, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.

Coelho IM, Coelho VN, da Eduardo J, Luz S, Ochi LS, Guimarães FG, Rios E. A gpu deep learning metaheuristic based model for time series forecasting. Appl Energy. 2017;201:412–8.

Da'u A, Salim N. Recommendation system based on deep learning methods: a systematic review and new directions. Artif Intel Rev. 2020;53(4):2709–48.

Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process. 2014; p. 3.

Deng L, Dong Yu. Deep learning: methods and applications. Found Trends Signal Process. 2014;7(3–4):197–387.

Article   MathSciNet   MATH   Google Scholar  

Deng S, Li R, Jin Y, He H. Cnn-based feature cross and classifier for loan default prediction. In: 2020 International Conference on image, video processing and artificial intelligence, volume 11584, page 115841K. International Society for Optics and Photonics, 2020.

Dhyani M, Kumar R. An intelligent chatbot using deep learning with bidirectional rnn and attention model. Mater Today Proc. 2021;34:817–24.

Donahue J, Krähenbühl P, Darrell T. Adversarial feature learning. arXiv preprint arXiv:1605.09782, 2016.

Du K-L, Swamy MNS. Neural networks and statistical learning. Berlin: Springer Science & Business Media; 2013.

MATH   Google Scholar  

Dupond S. A thorough review on the current advance of neural network structures. Annu Rev Control. 2019;14:200–30.

Google Scholar  

Feng J, Yao Y, Lu S, Liu Y. Domain knowledge-based deep-broad learning framework for fault diagnosis. IEEE Trans Ind Electron. 2020;68(4):3454–64.

Garg S, Kaur K, Kumar N, Rodrigues JJPC. Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in sdn: a social multimedia perspective. IEEE Trans Multimed. 2019;21(3):566–78.

Géron A. Hands-on machine learning with Scikit-Learn, Keras. In: and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media; 2019.

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning, vol. 1. Cambridge: MIT Press; 2016.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Advances in neural information processing systems. 2014; p. 2672–680.

Google trends. 2021. https://trends.google.com/trends/ .

Gruber N, Jockisch A. Are gru cells more specific and lstm cells more sensitive in motive classification of text? Front Artif Intell. 2020;3:40.

Gu B, Ge R, Chen Y, Luo L, Coatrieux G. Automatic and robust object detection in x-ray baggage inspection using deep convolutional neural networks. IEEE Trans Ind Electron. 2020.

Han J, Pei J, Kamber M. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011.

Haykin S. Neural networks and learning machines, 3/E. London: Pearson Education; 2010.

He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell. 2015;37(9):1904–16.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2016; p. 770–78.

Hinton GE. Deep belief networks. Scholarpedia. 2009;4(5):5947.

Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.

Huang C-J, Kuo P-H. A deep cnn-lstm model for particulate matter (pm2. 5) forecasting in smart cities. Sensors. 2018;18(7):2220.

Huang H-H, Fukuda M, Nishida T. Toward rnn based micro non-verbal behavior generation for virtual listener agents. In: International Conference on human-computer interaction, 2019; p. 53–63. Springer.

Hulsebos M, Hu K, Bakker M, Zgraggen E, Satyanarayan A, Kraska T, Demiralp Ça, Hidalgo C. Sherlock: a deep learning approach to semantic data type detection. In: Proceedings of the 25th ACM SIGKDD International Conference on knowledge discovery & data mining, 2019; p. 1500–508.

Imamverdiyev Y, Abdullayeva F. Deep learning method for denial of service attack detection based on restricted Boltzmann machine. Big Data. 2018;6(2):159–69.

Islam MZ, Islam MM, Asraf A. A combined deep cnn-lstm network for the detection of novel coronavirus (covid-19) using x-ray images. Inf Med Unlock. 2020;20:100412.

Ismail WN, Hassan MM, Alsalamah HA, Fortino G. Cnn-based health model for regular health factors analysis in internet-of-medical things environment. IEEE. Access. 2020;8:52541–9.

Jangid H, Singhal S, Shah RR, Zimmermann R. Aspect-based financial sentiment analysis using deep learning. In: Companion Proceedings of the The Web Conference 2018, 2018; p. 1961–966.

Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Artif Intell Res. 1996;4:237–85.

Kameoka H, Li L, Inoue S, Makino S. Supervised determined source separation with multichannel variational autoencoder. Neural Comput. 2019;31(9):1891–914.

Karhunen J, Raiko T, Cho KH. Unsupervised deep learning: a short review. In: Advances in independent component analysis and learning machines. 2015; p. 125–42.

Kawde P, Verma GK. Deep belief network based affect recognition from physiological signals. In: 2017 4th IEEE Uttar Pradesh Section International Conference on electrical, computer and electronics (UPCON), 2017; p. 587–92. IEEE.

Kim J-Y, Seok-Jun B, Cho S-B. Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inf Sci. 2018;460:83–102.

Kingma DP, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

Kingma DP, Welling M. An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691, 2019.

Kiran PKR, Bhasker B. Dnnrec: a novel deep learning based hybrid recommender system. Expert Syst Appl. 2020.

Kloenne M, Niehaus S, Lampe L, Merola A, Reinelt J, Roeder I, Scherf N. Domain-specific cues improve robustness of deep learning-based segmentation of ct volumes. Sci Rep. 2020;10(1):1–9.

Kohonen T. The self-organizing map. Proc IEEE. 1990;78(9):1464–80.

Kohonen T. Essentials of the self-organizing map. Neural Netw. 2013;37:52–65.

Kök İ, Şimşek MU, Özdemir S. A deep learning model for air quality prediction in smart cities. In: 2017 IEEE International Conference on Big Data (Big Data), 2017; p. 1983–990. IEEE.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. 2012; p. 1097–105.

Latif S, Rana R, Younis S, Qadir J, Epps J. Transfer learning for improving speech emotion classification accuracy. arXiv preprint arXiv:1801.06353, 2018.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.

Li B, François-Lavet V, Doan T, Pineau J. Domain adversarial reinforcement learning. arXiv preprint arXiv:2102.07097, 2021.

Li T-HS, Kuo P-H, Tsai T-N, Luan P-C. Cnn and lstm based facial expression analysis model for a humanoid robot. IEEE Access. 2019;7:93998–4011.

Liu C, Cao Y, Luo Y, Chen G, Vokkarane V, Yunsheng M, Chen S, Hou P. A new deep learning-based food recognition system for dietary assessment on an edge computing service infrastructure. IEEE Trans Serv Comput. 2017;11(2):249–61.

Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017;234:11–26.

López AU, Mateo F, Navío-Marco J, Martínez-Martínez JM, Gómez-Sanchís J, Vila-Francés J, Serrano-López AJ. Analysis of computer user behavior, security incidents and fraud using self-organizing maps. Comput Secur. 2019;83:38–51.

Lopez-Martin M, Carro B, Sanchez-Esguevillas A. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Syst Appl. 2020;141:112963.

Ma X, Yao T, Menglan H, Dong Y, Liu W, Wang F, Liu J. A survey on deep learning empowered iot applications. IEEE Access. 2019;7:181721–32.

Makhzani A, Frey B. K-sparse autoencoders. arXiv preprint arXiv:1312.5663, 2013.

Mandic D, Chambers J. Recurrent neural networks for prediction: learning algorithms, architectures and stability. Hoboken: Wiley; 2001.

Book   Google Scholar  

Marlin B, Swersky K, Chen B, Freitas N. Inductive principles for restricted boltzmann machine learning. In: Proceedings of the Thirteenth International Conference on artificial intelligence and statistics, p. 509–16. JMLR Workshop and Conference Proceedings, 2010.

Masud M, Muhammad G, Alhumyani H, Alshamrani SS, Cheikhrouhou O, Ibrahim S, Hossain MS. Deep learning-based intelligent face recognition in iot-cloud environment. Comput Commun. 2020;152:215–22.

Memisevic R, Hinton GE. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural Comput. 2010;22(6):1473–92.

Article   MATH   Google Scholar  

Minaee S, Azimi E, Abdolrashidi AA. Deep-sentiment: sentiment analysis using ensemble of cnn and bi-lstm models. arXiv preprint arXiv:1904.04206, 2019.

Naeem M, Paragliola G, Coronato A. A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment. Expert Syst Appl. 2021;168:114285.

Niu Z, Zhong G, Hui Yu. A review on the attention mechanism of deep learning. Neurocomputing. 2021;452:48–62.

Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2009;22(10):1345–59.

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32:8026–37.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825–30.

MathSciNet   MATH   Google Scholar  

Pi Y, Nath ND, Behzadan AH. Convolutional neural networks for object detection in aerial imagery for disaster response and recovery. Adv Eng Inf. 2020;43:101009.

Piccialli F, Giampaolo F, Prezioso E, Crisci D, Cuomo S. Predictive analytics for smart parking: A deep learning approach in forecasting of iot data. ACM Trans Internet Technol (TOIT). 2021;21(3):1–21.

Puterman ML. Markov decision processes: discrete stochastic dynamic programming. Hoboken: Wiley; 2014.

Qu X, Lin Y, Kai G, Linru M, Meng S, Mingxing K, Mu L, editors. A survey on the development of self-organizing maps for unsupervised intrusion detection. Mob Netw Appl. 2019; p. 1–22.

Rahman MW, Tashfia SS, Islam R, Hasan MM, Sultan SI, Mia S, Rahman MM. The architectural design of smart blind assistant using iot with deep learning paradigm. Internet of Things. 2021;13:100344.

Ren J, Green M, Huang X. From traditional to deep learning: fault diagnosis for autonomous vehicles. In: Learning control. Elsevier. 2021; p. 205–19.

Rifai S, Vincent P, Muller X, Glorot X, Bengio Y. Contractive auto-encoders: Explicit invariance during feature extraction. In: Icml, 2011.

Rosa RL, Schwartz GM, Ruggiero WV, Rodríguez DZ. A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Trans Ind Inf. 2018;15(4):2124–35.

Sarker IH. Context-aware rule learning from smartphone data: survey, challenges and future directions. J Big Data. 2019;6(1):1–25.

Article   MathSciNet   Google Scholar  

Sarker IH. A machine learning based robust prediction model for real-life mobile phone data. Internet of Things. 2019;5:180–93.

Sarker IH. Cyberlearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet of Things. 2021;14:100393.

Sarker IH. Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Comput Sci. 2021.

Sarker IH. Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Computer. Science. 2021;2(3):1–16.

MathSciNet   Google Scholar  

Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN Computer. Science. 2021;2(3):1–21.

Sarker IH, Abushark YB, Alsolami F, Khan AI. Intrudtree: a machine learning based cyber security intrusion detection model. Symmetry. 2020;12(5):754.

Sarker IH, Abushark YB, Khan AI. Contextpca: Predicting context-aware smartphone apps usage based on machine learning techniques. Symmetry. 2020;12(4):499.

Sarker IH, Colman A, Han J. Recencyminer: mining recency-based personalized behavior from contextual smartphone data. J Big Data. 2019;6(1):1–21.

Sarker IH, Colman A, Han J, Khan AI, Abushark YB, Salah K. Behavdt: a behavioral decision tree learning to build user-centric context-aware predictive model. Mob Netw Appl. 2020;25(3):1151–61.

Sarker IH, Colman A, Kabir MA, Han J. Individualized time-series segmentation for mining mobile phone user behavior. Comput J. 2018;61(3):349–68.

Sarker IH, Furhad MH, Nowrozy R. Ai-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Computer. Science. 2021;2(3):1–18.

Sarker IH, Hoque MM, Uddin MK. Mobile data science and intelligent apps: concepts, ai-based modeling and research directions. Mob Netw Appl. 2021;26(1):285–303.

Sarker IH, Kayes ASM. Abc-ruleminer: User behavioral rule-based machine learning method for context-aware intelligent services. J Netw Comput Appl. 2020;168:102762.

Sarker IH, Kayes ASM, Badsha S, Alqahtani H, Watters P, Ng A. Cybersecurity data science: an overview from machine learning perspective. J Big data. 2020;7(1):1–29.

Sarker IH, Kayes ASM, Watters P. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data. 2019;6(1):1–28.

Sarker IH, Salah K. Appspred: predicting context-aware smartphone apps using random forest learning. Internet of Things. 2019;8:100106.

Satt A, Rozenberg S, Hoory R. Efficient emotion recognition from speech using deep learning on spectrograms. In: Interspeec, 2017; p. 1089–1093.

Sevakula RK, Singh V, Verma NK, Kumar C, Cui Y. Transfer learning for molecular cancer classification using deep neural networks. IEEE/ACM Trans Comput Biol Bioinf. 2018;16(6):2089–100.

Sujay Narumanchi H, Ananya Pramod Kompalli Shankar A, Devashish CK. Deep learning based large scale visual recommendation and search for e-commerce. arXiv preprint arXiv:1703.02344, 2017.

Shao X, Kim CS. Multi-step short-term power consumption forecasting using multi-channel lstm with time location considering customer behavior. IEEE Access. 2020;8:125263–73.

Siami-Namini S, Tavakoli N, Namin AS. The performance of lstm and bilstm in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data), 2019; p. 3285–292. IEEE.

Ślusarczyk B. Industry 4.0: are we ready? Pol J Manag Stud. 2018; p. 17

Sumathi P, Subramanian R, Karthikeyan VV, Karthik S. Soil monitoring and evaluation system using edl-asqe: enhanced deep learning model for ioi smart agriculture network. Int J Commun Syst. 2021; p. e4859.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2015; p. 1–9.

Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A survey on deep transfer learning. In: International Conference on artificial neural networks, 2018; p. 270–279. Springer.

Vesanto J, Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Netw. 2000;11(3):586–600.

Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A, Bottou L. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res. 2010;11(12).

Wang J, Liang-Chih Yu, Robert Lai K, Zhang X. Tree-structured regional cnn-lstm model for dimensional sentiment analysis. IEEE/ACM Trans Audio Speech Lang Process. 2019;28:581–91.

Wang S, Wan J, Li D, Liu C. Knowledge reasoning with semantic data for real-time data processing in smart factory. Sensors. 2018;18(2):471.

Wang W, Zhao M, Wang J. Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J Ambient Intell Humaniz Comput. 2019;10(8):3035–43.

Wang X, Liu J, Qiu T, Chaoxu M, Chen C, Zhou P. A real-time collision prediction mechanism with deep learning for intelligent transportation system. IEEE Trans Veh Technol. 2020;69(9):9497–508.

Wang Y, Huang M, Zhu X, Zhao L. Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on empirical methods in natural language processing, 2016; p. 606–615.

Wei P, Li Y, Zhang Z, Tao H, Li Z, Liu D. An optimization method for intrusion detection classification model based on deep belief network. IEEE Access. 2019;7:87593–605.

Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning. J Big data. 2016;3(1):9.

Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H, Wang C. Machine learning and deep learning methods for cybersecurity. Ieee access. 2018;6:35365–81.

Xu W, Sun H, Deng C, Tan Y. Variational autoencoder for semi-supervised text classification. In: Thirty-First AAAI Conference on artificial intelligence, 2017.

Xue Q, Chuah MC. New attacks on rnn based healthcare learning system and their detections. Smart Health. 2018;9:144–57.

Yousefi-Azar M, Hamey L. Text summarization using unsupervised deep learning. Expert Syst Appl. 2017;68:93–105.

Yuan X, Shi J, Gu L. A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst Appl. 2020;p. 114417.

Zhang G, Liu Y, Jin X. A survey of autoencoder-based recommender systems. Front Comput Sci. 2020;14(2):430–50.

Zhang X, Yao L, Huang C, Wang S, Tan M, Long Gu, Wang C. Multi-modality sensor data classification with selective attention. arXiv preprint arXiv:1804.05493, 2018.

Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D, Zhang Y. A survey on deep learning based brain computer interface: recent advances and new frontiers. arXiv preprint arXiv:1905.04149, 2019; p. 66.

Zhang Y, Zhang P, Yan Y. Attention-based lstm with multi-task learning for distant speech recognition. In: Interspeech, 2017; p. 3857–861.

Download references

Author information

Authors and affiliations.

Swinburne University of Technology, Melbourne, VIC, 3122, Australia

Chittagong University of Engineering & Technology, Chittagong, 4349, Bangladesh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Iqbal H. Sarker .

Ethics declarations

Conflict of interest.

The author declares no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K. N. and M. Shivakumar.

Rights and permissions

Reprints and permissions

About this article

Sarker, I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN COMPUT. SCI. 2 , 420 (2021). https://doi.org/10.1007/s42979-021-00815-1

Download citation

Received : 29 May 2021

Accepted : 07 August 2021

Published : 18 August 2021

DOI : https://doi.org/10.1007/s42979-021-00815-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Deep learning
  • Artificial neural network
  • Artificial intelligence
  • Discriminative learning
  • Generative learning
  • Hybrid learning
  • Intelligent systems
  • Find a journal
  • Publish with us
  • Track your research

A view of Artificial Neural Network

Ieee account.

  • Change Username/Password
  • Update Address

Purchase Details

  • Payment Options
  • Order History
  • View Purchased Documents

Profile Information

  • Communications Preferences
  • Profession and Education
  • Technical Interests
  • US & Canada: +1 800 678 4333
  • Worldwide: +1 732 981 0060
  • Contact & Support
  • About IEEE Xplore
  • Accessibility
  • Terms of Use
  • Nondiscrimination Policy
  • Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Research Paper on Basic of Artificial Neural Network

Profile image of International Journal  IJRITCC

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.

Related Papers

Mohammed Abdulla

—An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.

research papers on artificial neural networks pdf

Saidul Mursalin

Artificial Neural Networks (ANN): Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Basic Structure of ANNs: The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites.

IRJET Journal

An Artificial Neural Network (ANN) is a data processing paradigm inspired by the way biological nervous systems, such as the brain, process data. The unique structure of the information processing system is a crucial component of this paradigm. It is made up of a huge number of highly interconnected processing elements (neurons) that work together to solve issues. ANNs, like humans, learn by example, and a huge dataset results in more accuracy. Through a learning process, an ANN is trained for a specific application, such as pattern recognition or data classification. This is also true of ANNs. This paper provides an overview of Artificial Neural Networks (ANN), their working, and training. It also describes the application and benefits of ANN.

Md Tanjil Sarker

In this paper, we are expounding Artificial Neural Network or ANN, its different qualities and business applications. In this paper we additionally demonstrate that "what are neural systems" and "Why they are so essential in today's Artificial knowledge?" Because various advances have been made in creating Intelligent framework, some roused by natural neural systems. ANN gives an exceptionally energizing choices and other application which can assume imperative part in today's software, Computer engineering field. There are a few Limitations likewise which are said. An Artificial Neural Network (ANN) is a data handling worldview that is motivated by the way natural sensory systems, for example, the mind, prepare data. The key component of this worldview is the novel structure of the data preparing framework. It is made out of an extensive number of exceptionally interconnected handling components (neurons) working as one to take care of particular issues. ANNs, similar to individuals, learn by illustration. An ANN is designed for a particular application, for example, design acknowledgment or information arrangement, through a learning procedure. Learning in natural frameworks includes conformity to the synaptic associations that exist between the neurons. This is valid for ANNs too. This paper gives outline of Artificial Neural Network, working and preparing of ANN. It additionally clarifies the application and points of interest of ANN.

Furqan Y A Q U B khan

First step towards AI is taken by Warren McCulloch a neurophysist and a mathematician Walter Pitts. They modelled a simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise data. During the present study it was observed that trained neural network expert in analyzing the information has been provided with other advantages as Adaptive learning, Real Time operation, self-organization and Fault tolerance as well. Apart from convectional computing, neural networking use different processing units (Neurons) in parallel with each other. These need not to be programmed. They function just like human brain. We need to give it examples to solve different problems and these examples must be selected carefully so that it would not be waste of time.we use combination of neural networking and computational programming to achieve maximal efficiency right now but neural networking will eventually take over in future. We introduced artificial neural networking in which electronic models where used as neural structure of brain. Computers can store data as ledgers etc. but have difficulty in recognizing patterns but brain stores information as patterns. Further as artificial neural networking was introduced which has artificial neurons who act as real neurons and do functions as they do. They are used for speech, hearing, reorganization, storing information as patterns and many other functions which a human brain can do. These neural networks were combined and dynamically self-combined which is not true for any artificial networking. These neurons work as groups and sub divide the problem to resolve it. These are grouped in layers and it is art of engineering to make them solve real world problems. The most important thing is the connections between the neurons, it is glue to system as it is excitation inhibition process as the input remains constant one neuron excites while other inhibits as in subtraction addition process. Basically, all ANN have same network that is input, feedback or hidden and output.

Noor Danish , vartika sharma

IUPAC Standards Online

Adel El-Shahat

Mujeeb Rehman O

Zeeshan Bhatti

This article looks at the essentials for artificial intelligence and more specifically neural networking systems in today's competitive business world. Some core principles of neural network architecture are discussed, the advantages of such networks. The domain of commercial applications of neural technology has been highlighted. Neural networks have various applications and the potential that exists in various civil and military fields is tremendous.

RELATED PAPERS

The Journal of Urology

Nadim Ayoub , Alexia Even

Colombian Applied Linguistics Journal

Alejandro Galvis

Temitope Samuel

Hormones and Cancer

José Moisés Capetillo Velázquez

Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology

Samiran Nundy

Journal of Microencapsulation

AWANISH KUMAR

British Journal of Hospital Medicine

NISHIL PATEL

The Astrophysical Journal

Luis Colina

guillermo josé

Angiologia e Cirurgia Vascular

Vítor Ferreira

Revue d'Écologie (La Terre et La Vie)

Kpérkouma Wala

Journal of Higher Education Outreach and Engagement

Turkish Journal of Forestry

International Orthopaedics

Jennyfer Mitterer

2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)

International journal of electrochemistry

Nina Dimcheva

做USQ毕业证成绩单/南昆士兰大学假文凭 USQ学位证QQ微信2298881

ACM Transactions on Computer-Human Interaction

Peter Dalsgaard

Social Science Research Network

Luca Stanca

Acta Neurologica Scandinavica

Johan Bellner

  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Help | Advanced Search

Quantitative Biology > Neurons and Cognition

Title: neural networks, artificial intelligence and the computational brain.

Abstract: In recent years, several studies have provided insight on the functioning of the brain which consists of neurons and form networks via interconnection among them by synapses. Neural networks are formed by interconnected systems of neurons, and are of two types, namely, the Artificial Neural Network (ANNs) and Biological Neural Network (interconnected nerve cells). The ANNs are computationally influenced by human neurons and are used in modelling neural systems. The reasoning foundations of ANNs have been useful in anomaly detection, in areas of medicine such as instant physician, electronic noses, pattern recognition, and modelling biological systems. Advancing research in artificial intelligence using the architecture of the human brain seeks to model systems by studying the brain rather than looking to technology for brain models. This study explores the concept of ANNs as a simulator of the biological neuron, and its area of applications. It also explores why brain-like intelligence is needed and how it differs from computational framework by comparing neural networks to contemporary computers and their modern day implementation.

Submission history

Access paper:.

  • Download PDF

license icon

References & Citations

  • Google Scholar
  • Semantic Scholar

BibTeX formatted citation

BibSonomy logo

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

  • Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

COMMENTS

  1. (PDF) AN INTRODUCTION TO ARTIFICIAL NEURAL NETWORK

    AN INTRODUCTION TO ARTIFICIAL NEURAL NETWORK Authors: Kuldeep Shiruru Jain University Abstract and Figures Artificial Neural Network (ANN) is gaining prominence in various applications like...

  2. PDF Machine learning with neural networks

    arXiv:1901.05639v4 [cs.LG] 27 Oct 2021 Machine learning with neural networks BERNHARDMEHLIG Department of Physics University of Gothenburg Göteborg, Sweden 2021 ACKNOWLEDGEMENTS This textbook is based on lecture notes for the courseArtificial Neural Networks

  3. Introduction to Machine Learning, Neural Networks, and Deep Learning

    Over the past decade, artificial intelligence (AI) has become a popular subject both within and outside of the scientific community; an abundance of articles in technology and non-technology-based journals have covered the topics of machine learning (ML), deep learning (DL), and AI. 1-6 Yet there still remains confusion around AI, ML, and DL.

  4. Neural networks: An overview of early research, current frameworks and

    This paper presents a comprehensive overview of modelling, simulation and implementation of neural networks, taking into account that two aims have emerged in this area: the improvement of our understanding of the behaviour of the nervous system and the need to find inspiration from it to build systems with the advantages provided by nature to p...

  5. Artificial Neural Network and Its Application Research Progress in

    2 Principles and development history of artificial neural networks The artificial neural network imitates the human brain neuron network and abstracts it, and then establishes a certain mathematical model, and processes information by adjusting the interconnection between a large number of nodes in the model [7,8]. It has self-adaptive and self-

  6. Review of deep learning: concepts, CNN architectures, challenges

    Survey Paper Open access Published: 31 March 2021 Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie & Laith Farhan

  7. [1404.7828] Deep Learning in Neural Networks: An Overview

    Juergen Schmidhuber In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium.

  8. PDF Artificial Neural Network Systems

    entific research documents, whether they are pub-lished or not. The documents may come from ... forward the research filed by reviewing and analyzing recent achievements in the published papers. Thus, the developed ANN systems can be presente d and new methodologies and applications of the ... Artificial neural networks are designed in the same ...

  9. (PDF) Artificial Neural Networks: An Overview

    This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model....

  10. (PDF) Artificial Neural Network Systems

    In this paper, a review in recent development and applications of the Artificial Neural Networks is presented in order to move forward the research filed by reviewing and analyzing recent...

  11. Deep Learning: A Comprehensive Overview on Techniques ...

    Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today's Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various ...

  12. Predicting future earnings changes: a modest integration of artificial

    Open PDF in Browser. Share: Permalink. ... we employed artificial neural networks, and our results indicated that the performance of artificial neural networks is much better than that of random forests. Secondly, we used manually curated asset pricing factors, which are more evident, efficient, and have well-defined meanings for each factor ...

  13. An Optimal Experimental Design Framework for Fast Kinetic Model ...

    Recent works on this topic introduced a novel approach that employs artificial neural networks (ANNs) to identify kinetic models. In this paper, the ANNs-based model identification approach is expanded by introducing an optimal experimental design procedure.

  14. PDF 7. Artificial neural networks

    Artificial neural networks Introduction to neural networks Despite struggling to understand intricacies of protein, cell, and network function within the brain, neuroscientists would agree on the following simplistic description of how the brain computes: Basic units called "neurons" work in parallel, each performing some computation on its ...

  15. Introduction to artificial neural networks

    Introduction to artificial neural networks Abstract: A neural network is a data processing system consisting of a large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain.

  16. (PDF) A Review on Artificial Neural Networks

    PDF | This paper deals with the glance of introductory to Artificial Neural Networks. Neural networks simulate how the complex human brain works with... | Find, read and cite all the...

  17. [PDF] Artificial Neural Network : A Brief Overview

    1 Excerpt Artificial Neural Network: Understanding the Basic Concepts without Mathematics Su-Hyun Han K. Kim Sangyun Kim Y. Youn Computer Science, Medicine Dementia and neurocognitive disorders 2018 TLDR The purpose of this review is to explain the fundamental concepts of artificial neural networks. Expand 58

  18. [PDF] What are artificial neural networks?

    Computer Science, Engineering. Nature Electronics. 2023. TLDR. An artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid to perform tasks such as speech recognition and nonlinear equation prediction and illustrates the practical potential of this technique by using it ...

  19. [2402.10492v1] Developing an Optimal Model for Predicting the Severity

    Download PDF Abstract: This research utilized three types of artificial neural network (ANN) methodologies, namely Backpropagation Neural Network (BPNN) with varied training, transfer, divide, and learning functions; Radial Basis Function Neural Network (RBFNN); and General Regression Neural Network (GRNN), to forecast the severity of stem rust. It considered parameters such as mean maximum ...

  20. PDF ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATIONS

    This paper presents a survey of the research and explosive developments of many ANN-related applications. A brief overview of the ANN theory, models and applications is presented. Potential areas of applications are identified and future trend is discussed. Keywords: Black Box Modeling, Neural Network models, Neural Network applications

  21. A view of Artificial Neural Network

    A view of Artificial Neural Network Abstract: In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications. In this paper also show that "what are neural networks" and "Why they are so important in today's Artificial intelligence?"

  22. (PDF) Research Paper on Basic of Artificial Neural Network

    Mohammed Abdulla —An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system.

  23. (PDF) Introduction to artificial neural networks

    Jan 2024 Amany G Fadhil Hana Ali Zainab A Khalaf View Show abstract ... Using this model, a simulation environment was created to classify and predict the effective genotypes of cumin based on...

  24. [2101.08635] Neural Networks, Artificial Intelligence and the

    Neural networks are formed by interconnected systems of neurons, and are of two types, namely, the Artificial Neural Network (ANNs) and Biological Neural Network (interconnected nerve cells). The ANNs are computationally influenced by human neurons and are used in modelling neural systems.

  25. Research Paper on Basic of Artificial Neural Network

    1 A Comparative Study of Training Algorithms of Saumya Verma India Vaishnavi Gupta Computer Science 2016 TLDR This paper gives overview of artificial neural networks, their working, architecture, learning methods, how one can create and train their own neural network using MATLAB, and their applications. Expand 1 Excerpt

  26. (PDF) Neural Networks and Their Applications

    PDF | A survey research paper about Neural Networks and Their Applications | Find, read and cite all the research you need on ResearchGate

  27. Neural network

    Schematic of a simple feedforward artificial neural network. An artificial neural network is a mathematical model used to approximate nonlinear functions. While early artificial neural networks were physical machines, today they are almost always implemented in software. Neurons in an artificial neural network are usually arranged into layers, with information passing from the first layer (the ...