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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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“Methods based on convolutional neural networks (CNNs) (Krizhevsky et al., 2012; Rawat and Wang, 2017) can build complex deep-level features based on simple low-level features, making them competitive against classic shallow hand-crafted features approaches.”
BackgroundSelf-citationDesign of an Always-On Image Sensor Using an Analog Lightweight Convolutional Neural Network
Choi, J., Lee, S., Son, Y., & Kim, S. Y. (2020) · Sensors · doi:10.3390/s20113101 · Cited by 30
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“…of neurobiological networks, the deep neural network architectures that are now widely used for image analysis (LeCun, Bengio, & Hinton, 2015; Rawat & Wang, 2017; Goodfellow, Bengio, & Courville, 2016) and proposed as models of the visual system (Yamins, Hong, Cadieu, Solomon, Seibert, &…”
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Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms
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Multi-target CNN-LSTM regressor for predicting urban distribution of short-term food delivery demand
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Chang, H., Wang, Y.-C., Shih, Y.-Y., & Kuo, C. (2022) · Applied Sciences · doi:10.3390/app12084080 · Cited by 30
“The noted methods include CNN, RNN, GAN, etc.”
MethodClassification of Underwater Fish Images and Videos via Very Small Convolutional Neural Networks
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“Moreover, a convolutional layer drastically reduces the number of parameters in a deep neural model, especially compared to multi-layer perceptrons or, in general, fully-connected layers [16].”
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“Different from image classification [17], which computes image-level class probability, semantic segmentation aims to predict the class labels in a pixel-wise manner.”
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“Convolutional neural networks (CNNs), in particular, are commonly utilized in changing image categorization tasks and have shown substantial performance since 2012 [20].”
LMFRNet: A Lightweight Convolutional Neural Network Model for Image Analysis
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Hybrid convolutional long short‐term memory models for sales forecasting in retail
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“TL performs very effectively in determining new tasks and has recently achieved significant success in problems with datasets that do not have sufficient diversity, such as disease diagnosis in medicine [13, 27, 28].”
Patch match networks: Improved two-channel and Siamese networks for image patch matching
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“Deep convolutional architectures date back to the seminal paper [21], inspired by the visual cortex [22,23], which significantly extended the conventional theories of ensemble learning (see, for example, [24,25]) and polyhedral separability [26], and became a successful algorithmic framework in classification, pattern…”
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Remaining Useful Life Prediction Using Temporal Convolution with Attention
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What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review
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“It has been shown to overcome these challenges [45].”
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“Deep convolutional neural networks have achieved good performance on multiple classical machine learning tasks such as image classification (15,16), object detection (17) and sentence classification (18).”
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Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls
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Designing Social Robot for Adults Using Self-Determination Theory and AI Technologies
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MethodSelf-citationA cognitive deep learning approach for medical image processing
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E-DBPN: Enhanced Deep Back-Projection Networks for Remote Sensing Scene Image Superresolution
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“Additionally, L p-pooling [75] is another choice to gather other distinctive clues, which can be regarded as a tradeoff between the max-pooling and average-pooling.”
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“Max-pooling and average pooling [1] have been widely used in CNNs.”
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An Analysis of Body Language of Patients Using Artificial Intelligence
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“COVID-Net, has been created by [115], is a deep convolutional neural network (see, for example, [116]) that can diagnose coronavirus from chest X-RAY pictures.”
BackgroundA Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
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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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