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 hyperbolic T-distribution produces a high-resolution time-frequency image of Mandarin speech signals, so speech signal classification can be improved using the CNN model.
“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]
The Introduction discusses CNNs for visual recognition, and the References list a work on speech recognition, but neither passage addresses hyperbolic T-distribution images of Mandarin speech or improved classification using them. Searches for the distribution, Mandarin speech, and CNN-based speech classification found no passage making the attributed claim. (LLM, conf=0.74)
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
(2014) · Cited by 33,419 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
(2010) · Cited by 14,134 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,745 · see citing works
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741 · see citing works
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Instance Normalization: The Missing Ingredient for Fast StylizationPreprint
Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016) · arXiv.org · doi:10.48550/arxiv.1607.08022 · Cited by 4,239 · see citing works
(2013) · Cited by 3,740 · see citing works
(1989) · neural information processing systems · Cited by 3,736 · see citing works
Wu, Y., & He, K. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_1 · Cited by 3,601 · see citing works
DeepPose: Human Pose Estimation via Deep Neural Networks
Toshev, A., & Szegedy, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.214 · Cited by 3,288 · see citing works
Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences
Werbos, P. J. (1974) · Medical Entomology and Zoology · Cited by 2,772 · see citing works
Deep Networks with Stochastic Depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_39 · Cited by 2,104 · see citing works
On the Convergence of Adam and Beyond
Reddi, S. J., Kale, S., & Kumar, S. (2019) · arXiv · doi:10.48550/arxiv.1904.09237 · Cited by 1,771 · see citing works
Training Very Deep NetworksPreprint
Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06228 · Cited by 1,453 · see citing works
Regularization Theory and Neural Networks Architectures
Girosi, F., Jones, M., & Poggio, T. (1995) · Neural Computation · doi:10.1162/neco.1995.7.2.219 · Cited by 1,345 · see citing works
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229 · see citing works
Deep convolutional neural networks for LVCSR
Sainath, T. N., Mohamed, A., Kingsbury, B. E. D., & Ramabhadran, B. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639347 · Cited by 1,066 · see citing works
Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv · doi:10.48550/arxiv.1505.00387 · Cited by 912 · see citing works
Fukushima, K., & Miyake, S. (1982) · Pattern Recognition · doi:10.1016/0031-3203(82)90024-3 · Cited by 746 · see citing works
Dual Path NetworksPreprint
Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., & Feng, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01629 · Cited by 714 · see citing works
Self-citation
Flagged first, then by citations
2 of the 52 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- [27]27. M. Lin, Q. Chen, and S. Yan, "Network in network," in Int. Conf. Learn. Represent. (2014).
- [41]41. A. L. Maas, A. Y. Hannun, and A. Y. Ng, "Rectifier nonlinearities improve neural network acoustic models," in Int. Conf. Mach. Learn. (2013).
- [42]42. R. Girshick, "Fast R-CNN," in IEEE Int. Conf. Comput. Vision, pp. 1440-1448 (2015).
- [43]43. K. He et al., "Mask R-CNN," in IEEE Int. Conf. Comput. Vision, pp. 2980-2988 (2017).
- [53]53. Wei Wang received his BS, MS, and PhD degrees in information and communication engineering from the National University of Defense Technology, China, in 1997, 2003, and 2010, respectively. He is currently a professor at the College of Computer and Communication, Changsha University of Science and Technology, Optical Engineering 040901-18 April 2019 • Vol. 58
- [58]58. Ji Li received his BS degree from Beijing Information Science and Technology University, China, in 2002, and his PhD from Wuhan University, China, in 2010, respectively. He is currently a lecturer at the College of Computer and Communication, Changsha University of Science and Technology, China. His research interests include signal processing, computer vision, and pattern recognition. Optical Engineering 040901-19 April 2019 • Vol. 58
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Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121 · see citing works
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“The HTD has been found to produce a high-resolution TF image Sensors 2021 , 21 , 2582 6 of 15 of Mandarin speech signals; hence, it the speech signal classification can be improved using the CNN model [10,31–33]. neural networks library [38].”
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Jalili, A., Gheisari, M., Alzubi, J. A. A., Fernández‐Campusano, C., Kamalov, F., & Moussa, S. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101144 · Cited by 23 · see citing works
Xu, J., & Yu, X. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3358208 · Cited by 22 · see citing works
Attention-based CNNs for Image Classification: A Survey
Zheng, M., Xu, J., Shen, Y., Tian, C., Li, J., Fei, L., Zong, M., & Liu, X. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2171/1/012068 · Cited by 22 · see citing works
Self-citationSemi-Self-Supervised Learning for Semantic Segmentation in Images with Dense Patterns
Najafian, K., Ghanbari, A., Kish, M. S., Eramian, M., Shirdel, G. H., Stavness, I., Jin, L., & Maleki, F. (2023) · Plant Phenomics · doi:10.34133/plantphenomics.0025 · Cited by 22 · see citing works
Nugraha, G. S., Darmawan, M. I., & Dwiyansaputra, R. (2023) · Kinetik Game Technology Information System Computer Network Computing Electronics and Control · doi:10.22219/kinetik.v8i2.1667 · Cited by 22 · see citing works
One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order
Zhang, J., Li, D., Wang, L., & Zhang, L. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500295 · Cited by 22 · see citing works
Application of Deep Learning for Imaging‐Based Stream Gaging
Boomen, R. L. V., Yu, Z., & Liao, Q. (2021) · Water Resources Research · doi:10.1029/2021wr029980 · Cited by 21 · see citing works
Gao, X., Huang, C., Teng, S., & Chen, G. (2022) · Applied Sciences · doi:10.3390/app12189244 · Cited by 20 · see citing works
Demystifying Bitcoin Address Behavior via Graph Neural Networks
Huang, Z., Huang, Y., Qian, P., Chen, J., & He, Q. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00137 · Cited by 23 · see citing works
Research on food safety sampling inspection system based on deep learning
Chen, T.-C., & Yu, S. (2021) · Food Science and Technology · doi:10.1590/fst.29121 · Cited by 20 · see citing works
Li, X., Ning, H., Huang, X., Dadashova, B., Kang, Y., & Ma, A. (2021) · Cartography and Geographic Information Science · doi:10.1080/15230406.2021.1992299 · Cited by 19 · see citing works
Zhong, Y., Dan, Y., Cai, Y., Lin, J., Huang, X., Mahmoud, O., Hald, E. S., Kumar, A., Fang, Q., & Mahmoud, S. S. (2023) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3328435 · Cited by 19 · see citing works
Automatic CNN-Based Enhancement of 360° Video Experience With Multisensorial Effects
Sexton, J. P., Simiscuka, A. A., McGuinness, K., & Muntean, G. (2021) · IEEE Access · doi:10.1109/access.2021.3115701 · Cited by 19 · see citing works
Remote sensing landslide target detection method based on improved Faster R-CNN
Yang, D., & Mao, Y. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.044521 · Cited by 18 · see citing works
CNN Based Image Classification of Malicious UAVs
Brown, J., Gharineiat, Z., & Raj, N. (2022) · Applied Sciences · doi:10.3390/app13010240 · Cited by 18 · see citing works
Brief Survey of Single Image Super-Resolution Reconstruction Based on Deep Learning Approaches
Wang, W., Hu, Y., Luo, Y., & Zhang, T. (2020) · Sensing and Imaging · doi:10.1007/s11220-020-00285-4 · Cited by 18 · see citing works
Self-citationSelf-Supervised Bi-Channel Transformer Networks for Computer-Aided Diagnosis
Gong, R., Han, X., Wang, J., Ying, S., & Shi, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3153902 · Cited by 17 · see citing works
Neural Network-based Pattern Recognition in the Framework of Edge Computing
Ning, J. (2024) · Romanian Journal of Information Science and Technology · doi:10.59277/romjist.2024.1.08 · Cited by 17 · see citing works
High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks
Wang, W., Zhang, C., Tian, J., Wang, X., Ou, J., Zhang, J., & Li, J. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8893419 · Cited by 17 · see citing works
Self-citationClassification of error-related potentials evoked during stroke rehabilitation training
Kumar, A., Pirogova, E., Mahmoud, S. S., & Fang, Q. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac1d32 · Cited by 17 · see citing works
A SAR Image Target Recognition Approach via Novel SSF-Net Models
Wang, W., Zhang, C., Tian, J., Ou, J., & Li, J. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8859172 · Cited by 17 · see citing works
Self-citationAlsharman, N., Saaidah, A., Almomani, O., Jawarneh, I., & Al-Qaisi, L. (2022) · Security and Communication Networks · doi:10.1155/2022/4375232 · Cited by 16 · see citing works
Joo, Y., Moon, S., & Choi, C. (2023) · IEEE Access · doi:10.1109/access.2023.3302271 · Cited by 15 · see citing works
Multimodal movie genre classification using recurrent neural network
Behrouzi, T., Toosi, R., & Akhaee, M. A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13418-6 · Cited by 15 · see citing works
Deep convolutional cross-connected kernel mapping support vector machine based on SelectDropout
Wang, Q., Liu, Z., Zhang, T., Alasmary, H., Waqas, M. M., Halim, Z., & Li, Y. (2023) · Information Sciences · doi:10.1016/j.ins.2023.01.004 · Cited by 15 · see citing works
CDF‐net: A convolutional neural network fusing frequency domain and spatial domain features
Yang, A., Li, M., Wu, Z., He, Y., Qiu, X., Song, Y. C., Du, W., & Gou, Y. (2023) · IET Computer Vision · doi:10.1049/cvi2.12167 · Cited by 14 · see citing works
Foreign objects detection using deep learning techniques for graphic card assembly line
Kuo, R. J., & Nursyahid, F. F. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01980-7 · Cited by 14 · see citing works
Tong, J., Xiang, L., Zhang, A. A., Miao, X. F., Wang, M., & Ye, P. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5997 · Cited by 13 · see citing works
Analysis of Deep Learning Architectures for Object Detection - A Critical Review
Pandiya, M., Dassani, S., & Mangalraj, P. (2020) · IEEE-HYDCON · doi:10.1109/hydcon48903.2020.9242776 · Cited by 13 · see citing works
Mao, X., Liu, K.-K., Cao, Q., Song, S.-Y., Liang, Y., Hu, Y., Chang, S., Liao, J., & Shan, C. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c00414 · Cited by 13 · see citing works
An Overview of Medical Imaging Techniques for Knee Osteoarthritis Disease
Vashishtha, A., & Acharya, A. K. (2021) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/2192 · Cited by 13 · see citing works
Pfeil, J., Siptroth, J., Pospisil, H., Frohme, M., Hufert, F. T., Moskalenko, O., Yateem, M., & Nechyporenko, A. S. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010051 · Cited by 12 · see citing works
Tan, X. L., He, J., Li, H., Jiang, J., He, Z., Guo, J., & Qiu, B. (2024) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2024.106458 · Cited by 12 · see citing works
Wang, Z., Wang, K., Wang, X., Pan, S., & Qiao, X. (2022) · International journal of agricultural and biological engineering · doi:10.25165/j.ijabe.20221501.6313 · Cited by 12 · see citing works
Self-citationA COVID‐19 CXR image recognition method based on MSA‐DDCovidNet
Wang, W., Huang, W., Wang, X., Zhang, P., & Zhang, N. (2022) · IET Image Processing · doi:10.1049/ipr2.12474 · Cited by 12 · see citing works
Self-citationImproved Classification Model using CNN for Detection of Alzheimer’s Disease
Davuluri, R., & Ragupathy, R. (2022) · Journal of Computer Science · doi:10.3844/jcssp.2022.415.425 · Cited by 12 · see citing works
Stanojević, M., Drašković, D., & Nikolić, B. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.3.032004 · Cited by 14 · see citing works
Krishnasamy, N., & Ponnusamy, T. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22974 · Cited by 11 · see citing works
A COVID-19 CXR Image Recognition Method Based on Deep Transfer Learning
An, J., Zhang, N., Mahmoud, W. H., & Denis, M. F. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650886 · Cited by 11 · see citing works
Cıhan, M., Ceylan, M., Soylu, H., & Konak, M. (2021) · IRBM · doi:10.1016/j.irbm.2021.06.009 · Cited by 11 · see citing works
Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning
Peng, S., Chen, Y., Yang, J.-Y., Wang, K., & Tsai, J. (2022) · Biomedicines · doi:10.3390/biomedicines10010122 · Cited by 11 · see citing works
McAtee, P. D., Bukkapatnam, S., & Lakhtakia, A. (2019) · Journal of Nanophotonics · doi:10.1117/1.jnp.13.046006 · Cited by 11 · see citing works
Sokół, S., Pawuś, D., Majewski, P., & Krok, M. (2022) · Applied Sciences · doi:10.3390/app12199829 · Cited by 10 · see citing works
A Survey on 3D Scene Graphs: Definition, Generation and Application
Bae, J., Shin, D., Ko, K., Lee, J., & Kim, U.-H. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26889-2_13 · Cited by 10 · see citing works
A Convolutional Neural Network Ensemble for Flower Image Classification
Wang, Z., Wang, K., Wang, X., & Pan, S. (2020) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3436369.3437427 · Cited by 10 · see citing works
Self-citationSCU-Net: Semantic Segmentation Network for Learning Channel Information on Remote Sensing Images
Wang, W., Kang, Y., Liu, G., & Wang, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8469415 · Cited by 10 · see citing works
Self-citationCooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks
Molina-Tenorio, Y., Prieto‐Guerrero, A., Aguilar-González, R., & López‐Benítez, M. (2023) · Sensors · doi:10.3390/s23115209 · Cited by 10 · see citing works
He, X., Song, P., Huang, T., Wan, F., Kong, D., Zhai, R., Hua, C., & Dai, J. (2024) · Surfaces and Interfaces · doi:10.1016/j.surfin.2024.104003 · Cited by 9 · see citing works
A state‐of‐the‐art survey of predicting students' performance using artificial neural networks
Xiao, W. J., & Hu, J. (2023) · Engineering Reports · doi:10.1002/eng2.12652 · Cited by 9 · see citing works
- 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 · 2018: 1
- 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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