Cited by 3,503Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

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Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, Waseem, Wang, Zenghui·2017·Neural Computation

DOI 10.1162/neco_a_00990

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Abstract

Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.

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Grasp Classification With Weft Knit Data Glove Using a Convolutional Neural Network
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Background
A Neural Network Estimation of Ankle Torques From Electromyography and Accelerometry
Siu, H. C., Sloboda, J., McKindles, R. J., & Stirling, L. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3104761 · Cited by 40
Automated fruit grading using optimal feature selection and hybrid classification by self-adaptive chicken swarm optimization: grading of mango
Kumari, N., Dwivedi, R. K., Bhatt, A. K., & Belwal, R. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06473-x · Cited by 39
“Measures DT [44] KNN [37] SVM [43] NN [42] CNN [31] Fuzzy [32] FCNN [32] [31] SA-CSO-FCNN”
Method
Development of CNN Transfer Learning for Dyslexia Handwriting Recognition
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Cassava Leaf Disease Recognition Using Convolutional Neural Networks
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Infrared and visible image fusion based on residual dense network and gradient loss
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A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach
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“Convolutional layers in CNN work as feature extractors, so there is no need for a separate feature extraction step [38].”
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Automated seed identification with computer vision: challenges and opportunities
Zhao, L., Haque, S. M. R., & Wang, R. (2022) · Seed Science and Technology · doi:10.15258/sst.2022.50.1.s.05 · Cited by 40
A Systematic Review of Deep Learning Microalgae Classification and Detection
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“Image classification techniques are diverse from either traditional techniques or modern learning techniques [15].”
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Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTM
Poongadan, S., & Lineesh, M. C. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11622-z · Cited by 38
Palm Vein Recognition with Deep Hashing Network
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A deep learning approach to ship detection using satellite imagery
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“Since then, lots of research have been done on CNNs with improvement in various factors such as activation function, optimization technique, network architecture and regularization mechanism [17].”
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Convolutional neural networks for enhanced classification mechanisms of metamodels
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Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired
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From convolutional neural networks to models of higher‐level cognition (and back again)
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Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature
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“, involving image recognition (Rawat and Wang, 2017), natural language processing (Young et al.”
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Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts
Sunil, G. C., Saidul, B., Zhang, Y., Reed, D. D., Ahsan, M., Berg, E. P., & Sun, X. (2021) · Frontiers in Sensors · doi:10.3389/fsens.2021.654357 · Cited by 40
Unsafe Construction Behavior Classification Using Deep Convolutional Neural Network
Hung, P. D., & Su, N. T. (2021) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661821020073 · Cited by 38
Deep learning-based classification models for beehive monitoring
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Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences
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Deep learning-based on-line image analysis for continuous industrial crystallization processes
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Artificial Intelligence in Cryo-Electron Microscopy
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Method
A novel Siamese deep hashing model for histopathology image retrieval
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Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34
An Efficient Deep Learning Model for Eye Disease Classification
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Background
Ultra-low cost and high-performance paper-based flexible pressure sensor for artificial intelligent E-skin
Chen, Y., Wang, S., Liu, Y., Deng, H., Gao, H., Cao, M., Zhang, C., Cheng, X., & Xie, L. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.156293 · Cited by 34
Residual neural network-based fully convolutional network for microstructure segmentation
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“Recently, a variety of CNN algorithms have been developed and the accuracy has continued to show improvements [25].”
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Optimal Autonomous Driving Through Deep Imitation Learning and Neuroevolution
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Method
Artificial intelligence in the diagnosis, treatment and prevention of urinary stones
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Establishing a model to predict the single boll weight of cotton in northern Xinjiang by using high resolution UAV remote sensing data
Xu, W., Yang, W., Chen, S., Wu, C., Chen, P., & Lan, Y. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105762 · Cited by 34
Weakly-Supervised Image Hashing through Masked Visual-Semantic Graph-based Reasoning
Jin, L., Li, Z., Pan, Y., & Tang, J. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414022 · Cited by 33
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Method
Benchmarking Deep Learning Models for Driver Distraction Detection
Mase, J. M., Chapman, P., Figueredo, G. P., & Torres, M. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64580-9_9 · Cited by 36
A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index
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How to evaluate deep learning for cancer diagnostics – factors and recommendations
Daneshjou, R., He, B. D., Ouyang, D., & Zou, J. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188515 · Cited by 35
Detection of Cataract Based on Image Features Using Convolutional Neural Networks
Weni, I., Utomo, P. E. P., Hutabarat, B. F., & Alfalah, M. (2021) · IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · doi:10.22146/ijccs.61882 · Cited by 34
“It is an improved version of multilayer perceptron or other networks [6], [7].”
BackgroundMethod
Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides
Qu, H. Y., Minacapelli, C. D., Tait, C., Gupta, K., Bhurwal, A., Catalano, C., Dafalla, R. A., Metaxas, D., & Rustgi, V. K. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106153 · Cited by 38
CNN based Covid-aid: Covid 19 Detection using Chest X-ray
Singh, S., Sapra, P., Garg, A., & Vishwakarma, D. K. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418407 · Cited by 35
“CNN's usually demonstrate high-accuracy results in the area of image and object recognition [16] which is why we used this approach.”
Method
HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks
Kim, K. S., & Choi, Y. S. (2021) · Sensors · doi:10.3390/s21124054 · Cited by 35
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Method
A shallow convolutional neural network with elastic nets for blood glucose quantitative analysis using Raman spectroscopy
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Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia
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An optimized deep learning model using Mutation-based Atom Search Optimization algorithm for cervical cancer detection
Chitra, B., & Kumar, S. S. (2021) · Soft Computing · doi:10.1007/s00500-021-06138-w · Cited by 34
“Three major layers such as convolutional, activation, and pooling layer present in the convolutional base (Rawat and Wang 2017).”
Background
Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion
Rao, J., Yang, F., Mo, H., Kollmannsberger, S., & Rank, E. (2022) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2022.117418 · Cited by 34
“For a detailed survey on CNNs, we refer to [26, 27, 28, 29].”
Background
Evolving Deep Convolutional Neural Networks by Extreme Learning Machine and Fuzzy Slime Mould Optimizer for Real-Time Sonar Image Recognition
Guo, Y., Khishe, M., Mohammadi, M., Rashidi, S., & Nateri, M. S. (2021) · International Journal of Fuzzy Systems · doi:10.1007/s40815-021-01195-7 · Cited by 34
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Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices
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CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana
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An improved YOLOv5s method based bruises detection on apples using cold excitation thermal images
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Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing
Rosso, M. M., Aloisio, A., Randazzo, V., Tanzi, L., Cirrincione, G., & Marano, G. C. (2023) · Integrated Computer-Aided Engineering · doi:10.3233/ica-230709 · Cited by 34

Authors: Rawat, W., & Wang, Z.
Affiliations: University of South Africa (ZA)
ORCID: Wang, Zenghui
Venue: Neural Computation
Publisher: MIT Press
Published: 2017-09
Volume: 29
Issue: 9
Pages: 2352-2449
ISSN: 0899-7667 (ISSN-L)
1530-888X
DOI: 10.1162/neco_a_00990
OpenAlex: W2622826443
Cited by: 3,503
References: 286
Citations by year: 2026: 181 · 2025: 393 · 2024: 470 · 2023: 535 · 2022: 580 · 2021: 589 · 2020: 432 · 2019: 234 · 2018: 84 · 2017: 3
FWCI: 85.59
Type: journal article
Language: en
Open access: closed
Locations: Neural Computation (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords: convolutional neural networks, image classification, deep learning, history of neural networks, annotated dataset, computer vision

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Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

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