The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index.
Development of convolutional neural network and its application in image classification: a survey
Open accessAbstract
In recent years, convolutional neural networks (CNNs) have been widely used in various computer visual recognition tasks and have achieved good results compared with traditional methods. Image classification is one of the basic and important tasks of visual recognition, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. We first summarize the development history of CNNs and then analyze the architecture of various deep CNNs in image classification. Furthermore, not only the innovation of the network architecture is beneficial to the results of image classification, but also the improvement of the network optimization method or training method has improved the result of image classification. We also analyze each of these methods’ effect. The experimental results of various methods are compared. Finally, the development of future CNNs is prospected.
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“The HTD has been found to produce a high-resolution TF image of Mandarin speech signals; hence, it the speech signal classification can be improved using the CNN model [10,31,32,33].”
From Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment (2021) · cites this paper as [33]
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Cites
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Review of Image Classification Algorithms Based on Convolutional Neural Networks
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021) · Remote Sensing · doi:10.3390/rs13224712 · Cited by 893
Machine Learning for Structural Engineering: A State-of-the-Art Review
Thai, H.-T. (2022) · Structures · doi:10.1016/j.istruc.2022.02.003 · Cited by 748
Deep Learning for Medical Image-Based Cancer Diagnosis
Jiang, X., Hu, Z., Wang, S., & Zhang, Y. (2023) · Cancers · doi:10.3390/cancers15143608 · Cited by 409
A Novel Image Classification Approach via Dense-MobileNet Models
Wang, W., Li, Y., Zou, T., Wang, X., You, J., & Luo, Y. (2020) · Mobile Information Systems · doi:10.1155/2020/7602384 · Cited by 273
Self-citationA CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection
Wang, W., Tan, X., Zhang, P., & Wang, X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3198517 · Cited by 213
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Metadata
- Authors
- Wang, W., Yang, Y., Wang, X., Wang, W., & Li, J.
- Affiliations
- Changsha University of Science and Technology (CN)
- Venue
- Optical Engineering
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2019-04-11
- Volume
- 58
- Issue
- 04
- Pages
- 1-19
- ISSN
- 0091-3286 (ISSN-L)
- 1560-2303
- OpenAlex
- W2935849356
- Cited by
- 252
- References
- 52
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 43 · 2023: 56 · 2022: 55 · 2021: 37 · 2020: 30 · 2019: 3
- FWCI
- 13.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Optical Engineering (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, convolutional neural network architecture, training methods, object detection, object localization, semantic segmentation, visual recognition
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