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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Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods
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“In particular, Convolutional Neural Networks (CNNs)[23, 75] proved successful in a wide range of domains[49], from medical images[2, 95, 59] to robotics [1, 45] and cyber-security[98, 50], to name a few examples.”
Background
Autism Spectrum Disorder Detection in Children Via Deep Learning Models Based on Facial Images
Khan, B., Bhatti, S. M., & Akram, A. (2024) · Bulletin of Business and Economics (BBE) · doi:10.61506/01.00241 · Cited by 23
JellyNet: The convolutional neural network jellyfish bloom detector
Mcilwaine, B., & Casado, M. R. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2020.102279 · Cited by 24
“On top of the exceptionally high performance in image classification tasks (Rawat and Wang, 2017), CNNs have other key advantages.”
BackgroundMethodInfluential
Automatic classification of biofouling images from offshore renewable energy structures using deep learning
Signor, J., Schoefs, F., Quillien, N., & Damblans, G. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115928 · Cited by 22
Artificial intelligence and machine learning applications in the Spanish nuclear field
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MapReader: a framework for learning a visual language model for map analysis
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“In computer vision, analyzing image content has been a longstanding research focus, evolving from image classification (Figure 1(a)) (Rawat and Wang 2017) and semantic segmentation (Figure 1(b)) (Guo et al .”
Deep Learning-Based Detection of Tuberculosis Using a Gaussian Chest X-Ray Image Filter as a Software Lens
Eisentraut, L., Mai, C., Hosch, J., Benecke, A., Penava, P., & Buettner, R. (2025) · IEEE Access · doi:10.1109/access.2025.3544923 · Cited by 25
“Apart from a few approaches to detect TB based on techniques such as support vector machines [47] or C-means algorithms [48], the vast majority relies on neural networks, more precisely CNNs. CNNs are a deep learning architecture that generally performs well in classifying images [49].”
A Review on Gas Pipeline Leak Detection: Acoustic-Based, OGI-Based, and Multimodal Fusion Methods
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Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening
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Galaxy Image Classification Based on Citizen Science Data: A Comparative Study
Jiménez, M. A., Torres, M. T., John, R. I., & Triguero, I. (2020) · IEEE Access · doi:10.1109/access.2020.2978804 · Cited by 24
“More specifically, convolutional neural networks (CNNs) [27], [28] provide excellent solutions [29], being capable of taking a raw image as input and perform an implicit FE process along with the classification in one single step.”
Method
Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer Images
Benbrahim, H., Hachimi, H., & Amine, A. (2020) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v21i3.1725 · Cited by 22
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Background
Occupancy heat gain detection and prediction using deep learning approach for reducing building energy demand
Tien, P. W., Wei, S., Calautit, J. K., Darkwa, J., & Wood, C. J. (2020) · Journal of Sustainable Development of Energy Water and Environment Systems · doi:10.13044/j.sdewes.d8.0378 · Cited by 22
“Instead of performing complex pre-processing stages, the data can be used to derive directly and extract the acquired features from the selected parts of an image [33] .”
Method
Use of deep learning methods for hand fractures detection from plain hand radiographs
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“CNN’s are very successful in feature extraction and image classification, but numerous data are required for CNN training from scratch.[7] Data augmentation and transfer learning can be applied when the number of data is insufficient.”
Analysis of the sagittal root angle and its correlation with hard and soft tissue indices in anterior teeth for immediate implant evaluation: a retrospective study
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Convolutional Neural Networks for Differential Diagnosis of Raynaud’s Phenomenon Based on Hands Thermal Patterns
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Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows
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Quality control of apples by means of convolutional neural networks - Comparison of bruise detection by color images and near-infrared images
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Classification of UML Diagrams to Support Software Engineering Education
Tavares, J. F., Costa, Y. M. G., & Colanzi, T. E. (2021) · IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) · doi:10.1109/asew52652.2021.00030 · Cited by 22
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Method
Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure
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SILIC: A cross database framework for automatically extracting robust biodiversity information from soundscape recordings based on object detection and a tiny training dataset
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Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation
Kemenczky, P., Vakli, P., Somogyi, E., Homolya, I., Hermann, P., Gál, V., & Vidnyánszky, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-05583-3 · Cited by 23
Detecting Pine Trees Damaged by Wilt Disease Using Deep Learning Techniques Applied to Multi-Spectral Images
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“Although it is not the test result, Table 3 shows ResNet 18 gives the best accuracy with the smallest number of parameters [36] .”
Background
Characterization of uncertainties and model generalizability for convolutional neural network predictions of uranium ore concentrate morphology
Nizinski, C. A., Ly, C., Vachet, C., Hagen, A., Taşdizen, T., & McDonald, L. W. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104556 · Cited by 23
“and pooling layers which extract image features, next the fully-connected layers correlate extracted features to labels, and then an activation function computes a score for each image class [2].”
Background
QA-Filter: A QP-Adaptive Convolutional Neural Network Filter for Video Coding
Liu, C., Sun, H., Katto, J., Zeng, X., & Fan, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3152627 · Cited by 22
AI-Assisted Fusion of Scanning Electrochemical Microscopy Images Using Novel Soft Probe
Lin, Y.-H., Tsai, C.-N., Chen, P.-F., Lin, Y.-T., Darvishi, S., Girault, H. H., Lin, T., Liao, M., & Lin, T.-E. (2022) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.2c00032 · Cited by 22
The development of a convolutional neural network for the automatic detection of Northern Bobwhite Colinus virginianus covey calls
Nolan, V., Scott, C. D., Yeiser, J. M., Wilhite, N. G., Howell, P. E., Ingram, D. R., & Martin, J. A. (2022) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.294 · Cited by 22
Underwater Backscatter Recognition Using Deep Fuzzy Extreme Convolutional Neural Network Optimized via Hunger Games Search
Khishe, M., Mohammadi, M., & Varkani, A. R. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11068-1 · Cited by 22
“Numerous advantageous properties of DCNNs have increased their popularity in a variety of computer vision applications [62].”
Background
A robust cascaded deep neural network for image reconstruction of single plane wave ultrasound RF data
Wasih, M., Ahmad, S., & Almekkawy, M. K. (2023) · Ultrasonics · doi:10.1016/j.ultras.2023.106981 · Cited by 22
Efficient asynchronous federated learning with sparsification and quantization
Jia, J., Liu, J., Zhou, C., Tian, H., Dong, M., & Dou, D. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8002 · Cited by 22
A Novel Series Arc Fault Detection Method Based on CEEMDAN and IFAW-1DCNN
Wu, J., Wang, W., Shang, T., & Cao, J. (2024) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2024.3360222 · Cited by 22
“The number of convolution kernels determines the number of channels of the output feature [27].”
Background
Developing a Network Attack Detection System Using Deep Learning
Alsughayyir, B., Qamar, A. M., & Khan, R. (2019) · doi:10.1109/iccisci.2019.8716389 · Cited by 23
“Among them, the famous models are Deep Auto-encoders [23], Restricted Boltzmann Machines (RBMs) [24], Convolutional Neural Networks (CNNs) [25], Recurrent Neural Networks (RNNs) [26] and Long Short-Term Memory (LSTM) [27].”
Method
Estimation of neural network model parameters from local field potentials (LFPs)
Skaar, J.-E. W., Stasik, A., Hagen, E., Ness, T. V., & Einevoll, G. T. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007725 · Cited by 20
SPHIRE-crYOLO: A fast and accurate fully automated particle picker for cryo-EMPreprint
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Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural NetworksPreprint
Nedorubova, A., Kadyrova, A., & Khlyupin, A. N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12666 · Cited by 22
“3 Convolutional Neural Network Convolutional Neural Networks (or CNNs) is a commonly used class of feedforward learning algorithms [47].”
Background
Towards robust vision by multi-task learning on monkey visual cortexPreprint
Safarani, S., Nix, A., Willeke, K. F., Cadena, S. A., Restivo, K., Denfield, G. H., Tolias, A. S., & Sinz, F. H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.14344 · Cited by 25
Tuning Convolutional Neural Network Hyperparameters by Bare Bones Fireworks Algorithm
Tuba, I., Veinović, M. Đ., Tuba, E., Hrosik, R. C., & Tuba, M. (2022) · Studies in Informatics and Control · doi:10.24846/v31i1y202203 · Cited by 23
“…applications for solving problems in healthcare (Anwar et al., 2018; Pereira et al., 2016; Shen et al., 2017), security (Zheng et al., 2017), agriculture (Boulent et al., 2019), autonomous vehicles (Kebria et al., 2019), astronomy (Jia, Liu & Sun, 2020), and many other areas (Rawat & Wang, 2017).”
Background
Convergence of AI and Urban Emergency Responses: Emerging Pathway toward Resilient and Equitable Communities
Sun, L., Li, H., Nagel, J., & Yang, S. (2024) · Applied Sciences · doi:10.3390/app14177949 · Cited by 24
“…advancements in hardware, software, and algorithms (such as GPUs and DL frameworks like TensorFlow 2.17.0 and PyTorch 2.4.0) have solidi ﬁ ed CNNs as powerful tools for image classi ﬁ cation [94], object detection convolutional neural networks (CNNs), exempli ﬁ ed by the in ﬂ uential AlexNet.”
Influential
Latent Diffusion Models to Enhance the Performance of Visual Defect Segmentation Networks in Steel Surface Inspection
Leiñena, J., Saiz, F. A., & Barandiarán, Í. (2024) · Sensors · doi:10.3390/s24186016 · Cited by 30
“Among the many use cases of deep learning algorithms for superficial inspection, the most common ones are classification [7], region-based defect detection [8], and semantic segmentation [9,10].”
Background
Traffic Sign Detection and Quality Assessment Using YOLOv8 in Daytime and Nighttime Conditions
Aldoski, Z. N., & Koren, C. (2025) · Sensors · doi:10.3390/s25041027 · Cited by 26
“The introduction of machine learning and deep learning marked a transformative shift in the field, with Convolutional Neural Networks (CNNs) emerging as the dominant approach due to their superior ability to extract features and generalize across varying conditions [17,18].”
Deep Learning-Driven Analysis of Petrophysical Dynamics in Pay Zone Quality and Reservoir Characterization
Deng, C., Wang, Y., Mi, W., Xie, X., Sun, X., & Ghorbani, H. (2025) · Natural Resources Research · doi:10.1007/s11053-025-10490-1 · Cited by 21
“Their strength lies in capturing spatial hierarchies within data through the use of convolutional layers (Rawat & Wang, 2017).”
Radon Cumulative Distribution Transform Subspace Modeling for Image Classification
Shifat‐E‐Rabbi, M., Yin, X., Rubaiyat, A. H. M., Li, S., Kolouri, S., Aldroubi, A., Nichols, J. M., & Rohde, G. K. (2021) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-021-01052-0 · Cited by 23
“Methods based on hierarchical neural networks [19], such as convolutional neural networks (CNNs) [8] [20], have been widely studied recently given they have achieved top performance in certain classification tasks [19] [21] [22].”
Background
Convolutional Extreme Learning Machines: A Systematic Review
Rodrigues, I. R., Neto, S. R. D. S., Kelner, J., Sadok, D. F. H., & Endo, P. T. (2021) · Informatics · doi:10.3390/informatics8020033 · Cited by 26
“Image classification is the process of labeling images according to the information present in these images [2], and it is performed by recognizing patterns.”
Background
A Real-Time Semi-Supervised Deep Tone Mapping Network
Zhang, N., Zhao, Y., Wang, C., & Wang, R. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3089019 · Cited by 21
Towards more efficient CNN-based surgical tools classification using transfer learning
Jaafari, J., Douzi, S., Douzi, K., & Hssina, B. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00509-8 · Cited by 25
Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
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Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification
Yuan, H., Hong, C., Jiang, P.-T., Zhao, G., Tran, N. T. A., Xu, X., Yan, Y. Y., & Liu, N. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104673 · Cited by 11

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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