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Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
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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“Therefore, the output features include the specific pose information of objects, which means that CapsNet has the ability to recognize the orientation.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [186]
In the citing paper
“Some experiments have proved that CapsNet is able to reach state-ofthe-art results.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [186]
In the citing paper
“Although it did not achieve satisfactory results on complicated large-scale data sets, like CIFAR-100 or ImageNet, we can see that it is potential.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [186]
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“For more detailed mechanism illustrations and other variants of CNNs, readers can refer to these representative algorithm papers or surveys: (Krizhevsky et al., 2012; Zeiler and Fergus, 2014; Simonyan and Zisserman, 2014; Szegedy et al., 2015; He et al., 2016; Aloysius and Geetha, 2017; Rawat and Wang, 2017).”
BackgroundTree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning
Roy, D., Panda, P., & Roy, K. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.09.010 · Cited by 257
“In recent years Deep Convolutional Neural Networks (DCNNs) have emerged as the leading architecture for large scale image classification [19].”
BackgroundKattenborn, T., Eichel, J., & Fassnacht, F. E. (2019) · Scientific Reports · doi:10.1038/s41598-019-53797-9 · Cited by 279
“This may partly be explained by a general bottleneck of CNN training: the need for ample reference observations for identifying and learning the decisive image features 20,21 .”
BackgroundRoy, A. M., Bhaduri, J., Kumar, T., & Raj, K. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101919 · Cited by 277
Machine Learning for Microcontroller-Class Hardware: A Review
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Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
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“DCNNs brought great attention to the results of the ImageNet classification challenge [30].”
InfluentialexplAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
Spinner, T., Schlegel, U., Schäfer, H., & El‐Assady, M. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934629 · Cited by 291
“For example, DL methods outperform traditional algorithms for image processing [56] or natural language processing [82] and can often be applied by domain experts without prior ML expertise [12].”
MethodThe impact of patient clinical information on automated skin cancer detection
Pacheco, A. G. C., & Krohling, R. A. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103545 · Cited by 270
“Nowadays, it is the most successful deep learning methodology to handle the image classification task [44].”
BackgroundDeep Learning for Detecting Building Defects Using Convolutional Neural Networks
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“It was in mid-2000s when the developments in computing power and the emergence of large amounts of labelled datasets contributed to deep learning advancement and brought CNN back to light [51].”
BackgroundDeep learning as a tool for ecology and evolution
Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A. J., Valentini, G., & White, A. E. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13901 · Cited by 295
“Deep learning enabled breakthroughs in automated image classification, largely possible thanks to convolutional neural networks (CNNs; Box 3) (Goodfellow et al., 2016; Rawat and Wang, 2017).”
BackgroundVisual Attention-Driven Hyperspectral Image Classification
Haut, J. M., Paoletti, M. E., Plaza, J., Plaza, A., & Li, J. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2918080 · Cited by 255
“where poolz i,j represents the (i, j)-th output value of the pooling associated with the z-th feature map, and xz a,b denotes the (a, b)-th element contained by the pooling region Ri,j that encapsulates a spatial receptive field around the position (i, j) [72].”
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“1 Introduction In the past half-decade, machine learning enjoyed vast researches to achieve remarkable successes in a wide spectrum of scientific problems, including image processing [1, 2], cognitive science [3], genomics [4], drug discovery [5], and material designing [6], to name a few.”
BackgroundKhanam, R., Hussain, M., Hill, R. C., & Allen, P. (2024) · IEEE Access · doi:10.1109/access.2024.3425166 · Cited by 296
“This global connectivity enables the FC layer to perform a non-linear combination of selected features, extracting high-level abstractions and patterns crucial for accurate classification tasks [28].”
BackgroundMethodAdvances in site‐specific weed management in agriculture—A review
Gerhards, R., Sánchez, D. A., Hamouz, P., Peteinatos, G. G., Christensen, S., & Fernández‐Quintanilla, C. (2022) · Weed Research · doi:10.1111/wre.12526 · Cited by 235
“The causality of features and their weight on the classification are sometimes not completely understood in the CNNs, because they surpass the human-brain capacity to understand complex data (Rawat & Wang, 2017).”
BackgroundU-FLOOD – Topographic deep learning for predicting urban pluvial flood water depth
Löwe, R., Böhm, J., Jensen, D. G., Leandro, J., & Rasmussen, S. H. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126898 · Cited by 240
“Similar “ data augmentation ” procedures are commonly applied in image classification to reduce over-fitting (Rawat and Wang, 2017).”
BackgroundLiu, H., Zhang, C., Deng, Y., Xie, B., Liu, T., & Li, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3238548 · Cited by 235
Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226
“CNNs have been hugely successful in computer vision and excel at classification [31,44], object detection [45,46], and segmentation [47,48].”
MethodReview and empirical analysis of sparrow search algorithm
Yue, Y., Cao, L. J., Lu, D., Hu, Z., Xu, M., Wang, S., Li, B., & Ding, H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10435-1 · Cited by 226
Natural product drug discovery in the artificial intelligence era
Saldívar‐González, F. I., Aldas-Bulos, V. D., Medina‐Franco, J. L., & Plisson, F. (2021) · Chemical Science · doi:10.1039/d1sc04471k · Cited by 235
Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information
Hsieh, C., Chen, J., & Nien, B.-H. (2019) · IEEE Access · doi:10.1109/access.2019.2903487 · Cited by 220
Surface crack detection using deep learning with shallow CNN architecture for enhanced computation
Kim, B., Yuvaraj, N. R., Preethaa, K. R. S., & Pandian, R. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05690-8 · Cited by 226
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“The convolutional base includes three significant types of layers are: convolutional layers [31], activation layers [32], and pooling layers [33].”
BackgroundInformed training set design enables efficient machine learning-assisted directed protein evolution
Wittmann, B. J., Yue, Y., & Arnold, F. H. (2021) · Cell Systems · doi:10.1016/j.cels.2021.07.008 · Cited by 215
DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors
Gao, W., Zhang, L., Teng, Q., He, J., & Wu, H. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107728 · Cited by 214
Saqlain, M., Abbas, Q., & Lee, J. Y. (2020) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2020.2994357 · Cited by 212
“More details about each layer of CNN model can be found in [22].”
BackgroundDeep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey
Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., & Gao, Y. (2022) · Remote Sensing · doi:10.3390/rs14102385 · Cited by 230
Deep Dive into Machine Learning Models for Protein Engineering
Xu, Y., Verma, D., Sheridan, R. P., Liaw, A. I., Ma, J., Marshall, N., McIntosh, J. A., Sherer, E. C., Svetnik, V., & Johnston, J. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00073 · Cited by 210
Bilal, A., Zhu, L., Deng, A., Lu, H., & Wu, N. (2022) · Symmetry · doi:10.3390/sym14071427 · Cited by 222
“DL (Deep learning) is a powerful tool for automatically extracting features from images, and it has lately shown impressive accuracy in categorizing medical images [6].”
Artificial intelligence for precision oncology: beyond patient stratification
Azuaje, F. J. (2019) · npj Precision Oncology · doi:10.1038/s41698-019-0078-1 · Cited by 207
“Examples include: the detection and classification of skin lesions,(11) the identification and categorization of lung cancers,(14) and the detection of metastases in women with breast cancer,(15) all of which apply different versions of a DL technique known as convolutional neural networks (CNN).(12,16) Precision onco…”
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“Although these multi-layered networks are not a new technology (Rawat and Wang, 2017), the relatively recent advances in computing power have spurred an increase in their application for analyzing image-based data.”
BackgroundMethodTen deep learning techniques to address small data problems with remote sensing
Safonova, A. N., Ghazaryan, G., Stiller, S., Main-Knorn, M., Nendel, C., & Ryo, M. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103569 · Cited by 242
Li, G., Huang, Y., Chen, Z., Chesser, G. D., PURSWELL, J. L., Linhoss, J. E., & Zhao, Y. (2021) · Sensors · doi:10.3390/s21041492 · Cited by 219
“Previous investigators have reviewed CNNs used for semantic segmentation [16], image classification [17], and object detection [18] and provided information on development of CNN-based computer vision systems for generic purposes, but none of these reviews has focused on agriculture applications.”
BackgroundTrustworthy ai: A computational perspective
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261
“Although there are numerous definitions for AI [207], AI generally denotes a program or system that is able to cope with a real-world problem with human-like reasoning, for example, in the field of image recognition within AI, which uses deep learning networks to recognize objects or people within images [284].”
BackgroundIslam, M. M., Nooruddin, S., Karray, F., & Muhammad, G. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.01.015 · Cited by 196
Neuro-symbolic artificial intelligence: a survey
Bhuyan, B. P., Ramdane-Chérif, A., Tomar, R., & Singh, T. P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09960-z · Cited by 234
Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200
AI Applications through the Whole Life Cycle of Material Discovery
Li, J., Lim, K., Yang, H., Ren, Z., Raghavan, S., Chen, P., Buonassisi, T., & Wang, X. (2020) · Matter · doi:10.1016/j.matt.2020.06.011 · Cited by 198
The classification of construction waste material using a deep convolutional neural network
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Matek, C., Krappe, S., Münzenmayer, C., Haferlach, T., & Marr, C. (2021) · Blood · doi:10.1182/blood.2020010568 · Cited by 193
“11 However, the successful use of CNNs for image classi fi cation typically relies on the availability of a suf fi cient amount of high-quality image data and high-quality annotation, which can be dif fi cult to access because of the expense involved in obtaining labels by medical experts.”
Jin, X., Lu, J., Wang, S., Qi, H., & Li, S. (2018) · Remote Sensing · doi:10.3390/rs10030395 · Cited by 198
“For the neural network of the model, the convolutional layer and pooling layer refer to the Visual Geometry Group neural network (VGG network) [42], the kernel is 3 × 3, and the pooling layer is 2 × 2.”
MethodCompute Solution for Tesla's Full Self-Driving Computer
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Deep learning in analytical chemistry
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Deep Learning (CNN) and Transfer Learning: A Review
Gupta, J., Pathak, S. K., & Kumar, G. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2273/1/012029 · Cited by 224
“It has been found that the most modern DL approaches have performed well in a wide range of applications, including audio and voice, visual data, and natural language processing (NLP) [3], [4], [5], [6].”
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Metadata
- 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
- 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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