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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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Semantic segmentation model of cotton roots in-situ image based on attention mechanism
Kang, J., Liu, L., Zhang, F., Shen, C., Wang, N., & Shao, L. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106370 · Cited by 80
Visuospatial coding as ubiquitous scaffolding for human cognition
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Enhanced transfer learning with data augmentation
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ResultLi, Y., Bao, T., Xu, B., Shu, X., Zhou, Y., Du, Y., Wang, R., & Zhang, K. (2021) · Measurement · doi:10.1016/j.measurement.2021.110641 · Cited by 83
Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks
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Ship Classification Using Deep Learning Techniques for Maritime Target Tracking
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BackgroundThe Project Baseline Health Study: a step towards a broader mission to map human health
Arges, K., Assimes, T., Bajaj, V., Balu, S., Bashir, M. R., Beskow, L., Blanco, R., Califf, R., Campbell, P., Carin, L., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0290-y · Cited by 82
An improved human activity recognition technique based on convolutional neural network
Ravi Raj, K. G., & Kos, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-49739-1 · Cited by 93
“After that, descriptions from these performances feed one or several fully connected layers 32 .”
BackgroundEstimation of Citrus Maturity with Fluorescence Spectroscopy Using Deep Learning
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RETRACTED ARTICLE: Deep convolutional neural network for diabetes mellitus prediction
Alex, S. A., Nayahi, J. J. V., Shine, H. D., & Gopirekha, V. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06431-7 · Cited by 83
“CNN is popular for image classification [43].”
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How biological attention mechanisms improve task performance in a large-scale visual system model
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A machine learning model for detecting invasive ductal carcinoma with Google Cloud AutoML Vision
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Deep learning and cognitive science
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Automated spectroscopic modelling with optimised convolutional neural networks
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Brain-Machine Coupled Learning Method for Facial Emotion Recognition
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“Although the proposed method only utilizes a simple CNN network for feature extraction, the proposed method still can deliver better results.”
InfluentialContinuous estimation of power system inertia using convolutional neural networks
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Understanding the black-box: towards interpretable and reliable deep learning models
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“A CNN is often trained using methods like back propagation and gradient descent and has many layers of activations and convolutions dispersed among pooling layers ( Rawat & Wang, 2017 ).”
MethodDeep convolutional neural networks for eigenvalue problems in mechanics
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BackgroundNew High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey
Li, R., Napolitano, N. R., Tortora, C., Spiniello, C., Koopmans, L. V. E., Huang, Z., Roy, N., Vernardos, G., Chatterjee, S., Giblin, B., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab9dfa · Cited by 89
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“…the computer vision community, the deep convolutional neural network (CNN) (LeCun, Boser, and Denker et al. 1989) has been proposed recently to surmount various computer vision tasks, such as object detection, semantic, and instance segmentation (Rawat and Wang 2017; Li, Xu, and Lyu et al. 2019).”
BackgroundIntegrated Photonic Neural Networks: Opportunities and Challenges
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High-Quality Cloud Masking of Landsat 8 Imagery Using Convolutional Neural Networks
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“In the last decade, deep convolutional neural networks (CNN) have revolutionized image recognition [11].”
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“DNN have been using popularly in image classification problems with a convincing performances [20].”
MethodCalabrese, E., Rudie, J. D., Rauschecker, A. M., Villanueva-Meyer, J. E., Clarke, J. L., Solomon, D. A., & Cha, S. (2022) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdac060 · Cited by 78
Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics
Ota, R., & Yamashita, F. (2022) · Journal of Controlled Release · doi:10.1016/j.jconrel.2022.11.014 · Cited by 81
Strategies to Improve the Accuracy of Memristor-Based Convolutional Neural Networks
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“image recognition, classification, and detection tasks [8].”
Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion
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“CNN completes the classification and recognition task mainly through the study of the distribution of data [36,37].”
BackgroundA survey on fish classification techniques
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Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages
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Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic
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A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices
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“The most widely used neural network for deep learning work is the convolutional neural network, which converts unstructured image data into structured object tag data [5].”
MethodSolid Waste Image Classification Using Deep Convolutional Neural Network
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“Deep neural networks, especially CNN, have provided state-of-the-art solutions for many tasks including image classification [16,17], object detection [38], and semantic segmentation [39].”
BackgroundIntegration of machine learning and first principles models
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Monte Carlo DropBlock for modeling uncertainty in object detection
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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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