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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]
Citations
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
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
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
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
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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Flagged first, then by citations
12 of the 286 references couldn’t be matched to a known paper.
Research works printed but not linked: 86
- Abdulkader A., 2006, Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, 1
- Arora S., 2014, Proceedings of the 31th International Conference Machine Learning, 584
- Ba J., 2013, Advances in neural information processing systems, 26, 3084
- Ba J., 2015, Proceedings of the 3rd International Conference on Learning Representations, 1
- Bachman P., 2016, Advances in neural information processing systemsm, 29, 4826
- Baldi P., 2013, Advances in neural information processing systems, 26, 3084
- Belkin M., 2006, Journal of Machine Learning Research, 7, 2399
- Bengio Y., 2014, Proceedings of the 31st International Conference Machine Learning, 226
- Bulo S., 2014, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 81
- Chellapilla K., 2006, Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition.
- Chellapilla K., 2006, Proceedings of the 18th Annual Symposium on Electronic Imaging, 6067
- Chellapilla K., 2006, Proceedings of the 10th Internstional Workshop on Frontiers in Handwriting Recognition, 1
- Choromanska A., 2015, Proceedings 18th International Conference on Artificial Intelligence and Statistics, 192
- Clevert D., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Coates A., 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 215
- Collobert R., 2006, Journal of Machine Learning Research, 7, 1687
- Courbariaux M., 2015, Advances in neural information processing systems, 28, 3123
- Courbariaux M., 2016, Advances in neural information processing systems, 29, 1
- Deng L., 2014, APSIPA Transactions on Signal and Information Processing, 3, 1
- Denil M., 2013, Advances in neural information processing systems, 26, 2148
- Denton E. L., 2014, Advances in neural information processing systems, 27, 1269
- Dettmers T., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Duchi J., 2011, Journal of Machine Learning Research, 12, 2121
- Erhan D., 2010, Journal of Machine Learning Research, 11, 625
- Filho D. P., 2009, Proceedings of the XXII Brazilian Symposium on Computer Graphics and Image Processing, 1
- Fukushima K., 1979, Proceedings of the 6th International Joint Conference on Artificial Intelligence, 291
- Glorot X., 2010, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 249
- Glorot X., 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 315
- Goodfellow I., 2016, Deep learning
- Goodfellow I., 2014, Advances in neural information processing systems, 27, 2672
- Goodfellow I. J., 2015, Proceedings of the 3rd International Conference on Learning Representations, 1
- Goodfellow I. J., 2013, Proceedings of the 30th International Conference Machine Learning, 1319
- Han S., 2016, Proceedings of the 3rd International Conference on Learning Representations, 1
- Huang G. B., 2007, Labeled faces in the wild: A database for studying face recognition in unconstrained environments
- Iandola F. N., 2016, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 1MB model size
- Ioffe S., 2015, Proceedings of the 32nd International Conference Machine Learning, 448
- Jaderberg M., 2015, Advances in neural information processing systems, 28, 2017
- Jin J., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Karpathy A., 2016, CS231n: Convolutional neural networks for visual recognition
- Karpathy A., 2016, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3128
- Kennedy J., 1995, Proceedings of the 6th International Symposium on Micro Machine and Human Science, 39
- Krizhevsky A., 2012, Advances in neural information processing systems, 25, 1097
- Kulkarni T. D., 2015, Advances in neural information processing systemsm, 28, 2539
- Lebedev V., 2014, Speeding-up convolutional neural networks using fine-tuned CP-decomposition
- LeCun Y., 1989, Connections in perspective, 143
- LeCun Y., 1989, Advances in neural information processing systems, 2, 396
- Lee C., 2015, Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 562
- Leibo J. Z., 2011, Advances in neural information processing systems, 24, 711
- Levine S., 2016, Journal of Machine Learning Research, 17, 1
- Li S., 2016, Demystifying ResNet
- Li Z., 2016, Advances in neural information processing systemsm, 1
- Lin M., 2013, Network in network
- Linnainmaa S., 1970, The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors
- Liu W., 2016, Proceedings of the 33rd International Conference Machine Learning, 507
- Maas A. L., 2013, Proceedings of the 30th International Conference Machine Learning, 1
- Maharaj A. V., 2015, Improving the adversarial robustness of ConvNets by reduction of input dimensionality
- Mishkin D., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- National Data Science Bowl | Kaggle, 2016, Kaggle.com
- Netzer Y., 2011, Advances in neural information processing systems, 24 (NIPS) Workshop on Deep Learning and Unsupervised Feature Learning, 1
- Novikov A., 2015, Advances in neural information processing systems, 28
- Ranzato M., 2006, Advances in neural information processing systems, 19, 1137
- Recht B., 2011, Advances in neural information processing systems, 24, 693
- Rippel O., 2014, Proceedings of the 30th International Conference on Machine Learning, 1746
- Rippel O., 2015, Advances in neural information processing systems, 28, 2449
- Romero A., 2015, Proceedings of the 3rd International Conference on Learning Representations, 1
- Sabour S., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Sermanet P., 2012, Proceedings of the 21st International Conference on Pattern Recognition, 3288
- Snoek J., 2012, Advances in neural information processing systems, 25, 2951
- Sontag E. D., 1998, NATO ASI Series F Computer and Systems Sciences, 168, 69
- Srivastava N., 2014, Journal of Machine Learning Research, 15, 1929
- Srivastava N., 2013, Advances in neural information processing systems, 26, 2094
- Srivastava R. K., 2015, Advances in neural information processing systems, 28, 2377
- Srivastava R. K., 2015, Highway networks
- Steinkrau D., 2005, Proceedings of the 8th International Conference on Document Analysis and Recognition, 1115
- Stollenga M. F., 2014, Advances in neural information processing systems, 27, 3545
- Sun Y., 2014, Advances in neural information processing systems, 27, 1988
- Sutskever I., 2013, Proceedings of the 30th International Conference Machine Learning, 1139
- Szegedy C., 2014, Proceedings of the 1st International Conference on Learning Representations, 1
- Wager S., 2013, Advances in neural information processing systems, 26, 351
- Wan L., 2013, Proceedings of the 30th International Conference Machine Learning, 1058
- Wang S. I., 2013, Proceedings of the 30th International Conference Machine Learning, 118
- Wang Y., 2016, Advances in neural information processing systems, 29, 1
- Weinberger K. Q., 2005, Advances in neural information processing systems, 18, 1473
- Yang J., 2009, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1794
- Zhai S., 2016, Advances in neural information processing systems, 29, 1
- Zinkevich M., 2010, Advances in neural information processing systemsm, 23, 2595
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Pan, X., Lin, X., Cao, D., Zeng, X., Yu, P. S., He, L., Nussinov, R., & Cheng, F. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1597 · Cited by 202
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BackgroundTabian, I., Fu, H., & Khodaei, Z. S. (2019) · Sensors · doi:10.3390/s19224933 · Cited by 185
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BackgroundYoo, S., Lee, S., Kim, S., Hwang, K. H., Park, J., & Kang, N. (2021) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-021-02953-9 · Cited by 222
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Hasan, M. K., Elahi, M. T. E., Alam, M. A., Jawad, M. T., & Martí, R. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100819 · Cited by 190
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Almaraz-Damian, J. A., Ponomaryov, V. I., Sadovnychiy, S., & Castillejos, H. (2020) · Entropy · doi:10.3390/e22040484 · Cited by 179
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Naudé, W. A. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3568314 · Cited by 178
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Chai, Z., & Zhao, C. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2957232 · Cited by 166
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BackgroundMethodTassis, L. M., de Souza, J. E. T., & Krohling, R. A. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106191 · Cited by 172
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Mahmood, M. A., Vișan, A. I., Ristoscu, C., & Mihailescu, I. N. (2020) · Materials · doi:10.3390/ma14010163 · Cited by 172
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BackgroundZivkovic, M. Z., Bačanin, N., Antonijević, M., Nikolić, B., Kvaščev, G., Marjanović, M., & Savanović, N. (2022) · Electronics · doi:10.3390/electronics11223798 · Cited by 165
Deep Learning Based Systems Developed for Fall Detection: A Review
Islam, M. M., Tayan, O., Islam, M. R., Islam, M. S., Nooruddin, S., Kabir, M. N., & Islam, M. R. (2020) · IEEE Access · doi:10.1109/access.2020.3021943 · Cited by 168
“CNN architectures are great for finding patterns and shapes within given images [48], [52].”
BackgroundMethodA review of deep learning applications in human genomics using next-generation sequencing data
Alharbi, W., & Rashid, M. (2022) · Human Genomics · doi:10.1186/s40246-022-00396-x · Cited by 179
“The key principle of such deep construction is that massive processing and connection feature represents inferring nonlinear association between both inputs and outputs [109, 110].”
BackgroundCOVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks
Makris, A., Kontopoulos, I., & Tserpes, K. (2020) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3411408.3411416 · Cited by 165
“The widespread usage of CNNs for image classification tasks is due to the fact that they have demonstrated a high-accuracy performance in the fields of image recognition and object detection [5].”
BackgroundHyperspectral image classification using CNN: Application to industrial food packaging
Medus, L. D., Saban, M., Francés-Víllora, J. V., Bataller‐Mompeán, M., & Rosado-Muñoz, A. (2021) · Food Control · doi:10.1016/j.foodcont.2021.107962 · Cited by 159
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Perumal, T., Mustapha, N., Mohamed, R., & Shiri, F. M. (2024) · Journal on artificial intelligence · doi:10.32604/jai.2024.054314 · Cited by 629
“CNN pipeline for image classification [15].”
BackgroundAlzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier
Shankar, K., Lakshmanaprabu, S. K., Khanna, A. K., Tanwar, S., Rodrigues, J. J. P. C., & Roy, N. R. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.06.001 · Cited by 157
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Bermant, P. C., Bronstein, M. M., Wood, R. J., Gero, S., & Gruber, D. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-48909-4 · Cited by 164
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Shang, Z., Sun, L., Xia, Y., & We, Z. (2020) · Structural Health Monitoring · doi:10.1177/1475921720942836 · Cited by 156
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Feng, C., Zhang, J., Zhang, W., & Hodge, B. S. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118438 · Cited by 169
“CNN architectures are preferred over other deep learning architectures for the following reasons: (i) CNNs are more powerful than other architectures in image processing regarding their feature learning capabilities and performance, since convolutional layers are able to deal with inherent properties of images by usin…”
BackgroundEling, M., Nuessle, D., & Staubli, J. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-020-00201-7 · Cited by 156
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Zollanvari, A., Kunanbayev, K., Bitaghsir, S. A., & Bagheri, M. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3026497 · Cited by 154
“layers that can implicitly extract underlying patterns of data at increasingly more levels of abstraction [42], [43].”
BackgroundHu, T., Khishe, M., Mohammadi, M., Parvizi, G.-R., Karim, S. H. T., & Rashid, T. A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102764 · Cited by 153
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Dallora, A. L., Anderberg, P., Kvist, O., Mendes, E., Ruiz, S. D., & Berglund, J. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220242 · Cited by 152
Basiri, M. E., Abdar, M., Çifçi, M. A., Nemati, S., & Acharya, U. R. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105949 · Cited by 151
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Pyle, R. J., Bevan, R. L. T., Hughes, R. R., Rachev, R. K., Ali, A. A. S., & Wilcox, P. D. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.3045847 · Cited by 151
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BackgroundQuantum machine learning for chemistry and physics
Sajjan, M., Li, J., Selvarajan, R., Sureshbabu, S. H., Kale, S. S., Gupta, R., Singh, V., & Kais, S. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00203e · Cited by 173
Zhou, W., Kamata, S., Wang, H., Wong, M. S., & Hou, H. C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128751 · Cited by 151
Gesture recognition based on skeletonization algorithm and CNN with ASL database
Jiang, D., Li, G., Sun, Y., Kong, J., & Tao, B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6748-0 · Cited by 150
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Livieris, I. E., Pintelas, E. G., Stavroyiannis, S., & Pintelas, P. (2020) · Algorithms · doi:10.3390/a13050121 · Cited by 163
“Convolutional Neural Network (CNN) models [26,27] were originally proposed for image recognition problems, achieving human level performance in many cases.”
BackgroundA Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf
Arya, S., & Singh, R. K. (2019) · International Conference on Issues and Challenges in Intelligent Computing Techniques · doi:10.1109/icict46931.2019.8977648 · Cited by 148
Machine Learning in Computer Vision: A Review
Khan, A. A., Laghari, A. A., & Awan, S. A. (2018) · ICST Transactions on Scalable Information Systems · doi:10.4108/eai.21-4-2021.169418 · Cited by 184
“The main agenda behind the concept is to build strong AI which acts and think same as a human [54], CNN is an active learning algorithm [1], the objective is to recognized visual features of input image, adjust weight and bias (if needed) for differentiate object of one from others [55].”
BackgroundMSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation
Gao, G., Xu, G., Yu, Y., Xie, J., Yang, J. J., & Yue, D. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3098355 · Cited by 150
“Recently, deep convolutional neural networks (DCNNs) [3], [4] have shown their impressive capabilities on image classification with high resolution.”
BackgroundMachine learning for membrane design and discovery
Yin, H., Xu, M., Luo, Z., Bi, X., Li, J., Zhang, S., & Wang, X. (2022) · Green Energy & Environment · doi:10.1016/j.gee.2022.12.001 · Cited by 156
Zhai, Y., Yao, Y., Guan, Q., Liang, X., Li, X., Pan, Y., Yue, H., Yuan, Z., & Zhou, J. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1711915 · Cited by 146
“2015, Rawat and Wang 2017).”
BackgroundOyewola, D. O., Dada, E. G., Misra, S., & Damaševičius, R. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.352 · Cited by 149
Silent Speech Interfaces for Speech Restoration: A Review
González, J. A., Gomez-Alanis, A., Martín-Doñas, J. M., Pérez-Córdoba, J. L., & Gomez, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.3026579 · Cited by 147
“Recent advances in deep learning for image processing, especially in the case of CNNs [270], have paved the way for the application of these techniques in SSI research.”
BackgroundRarePlanes: Synthetic Data Takes Flight
Shermeyer, J., Hossler, T., Etten, A. V., Hogan, D., Lewis, R., & Kim, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00025 · Cited by 151
“Convolutional neural networks (CNNs), currently the leading class of algorithms for most vision tasks [43, 71], require a large amount of annotated observations.”
Li, Y., Yabuki, N., & Fukuda, T. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104140 · Cited by 139
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Hormel, T. T., Hwang, T. S., Bailey, S. T., Wilson, D. J., Huang, D., & Jia, Y. (2021) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.100965 · Cited by 144
Tran, M., Liu, M., Tran, Q., & Nguyen, T.-K. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3139706 · Cited by 145
“A deep learning method can suppress the aforementioned drawbacks of the traditional machine learning approaches [25], [26].”
MethodImproved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019) · Sensors · doi:10.3390/s19184021 · Cited by 138
“The sensory cognitive process of human brains can be well simulated via forward learning and feedback mechanism, thereby gradually improving the ability of traffic sign classification and recognition [33,34].”
BackgroundA bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126
“mental problem of computer vision in general [239, 240, 241] and a recurring theme also in bioimage analysis [242, 243, 244].”
BackgroundArtificial intelligence explainability: the technical and ethical dimensions
McDermid, J. A., Jia, Y., Porter, Z., & Habli, I. (2021) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2020.0363 · Cited by 136
“convolutional neural networks (CNNs) [7] and deep neural networks (DNN) [8].”
BackgroundChen, H., Li, C., Li, X., Rahaman, M. M., Hu, W., Li, Y., Liu, W., Sun, C., Sun, H., Huang, X., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105265 · Cited by 141
“It uses the small convolution kernel to enhance the extraction of spatial information better [66].”
MethodA multimodal hybrid parallel network intrusion detection model
Shi, S., Han, D., & Cui, M. (2023) · Connection Science · doi:10.1080/09540091.2023.2227780 · Cited by 141
“CNNs are a type of neural network that possess the characteristics of local connection and weight sharing and have been shown to achieve good results in the field of computer vision (D. Li et al., 2022a; Rawat & Wang, 2017; Shen et al., 2022, december).”
BackgroundAutomatic choroidal segmentation in OCT images using supervised deep learning methods
Kugelman, J., Alonso‐Caneiro, D., Read, S. A., Hamwood, J., Vincent, S. J., Chen, F. K., & Collins, M. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-49816-4 · Cited by 135
Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management
Ahmed, M. I. B., Alotaibi, R. B., Al-Qahtani, R. A., Al-Qahtani, R. S., Al-Hetela, S. S., Al-Matar, K. A., Al-Saqer, N. K., Rahman, A., Saraireh, L., Youldash, M. M., et al. (2023) · Sustainability · doi:10.3390/su151411138 · Cited by 132
Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model
Yang, H., Chen, L., Chen, M., Ma, Z., Deng, F., Li, M., & Li, X. (2019) · IEEE Access · doi:10.1109/access.2019.2958614 · Cited by 132
RingMo-SAM: A Foundation Model for Segment Anything in Multimodal Remote-Sensing Images
Yan, Z., Li, J., Li, X., Zhou, R., Zhang, W., Feng, Y., Diao, W., Fu, K., & Sun, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3332219 · Cited by 150
“Many deep convolutional neural networks (DCNNs) are capable of adaptively extracting both shallow and deep features from images to achieve excellent performance on semantic segmentation tasks [14], [15], [16], [17], [18].”
BackgroundDeep features based on a DCNN model for classifying imbalanced weld flaw types
Hou, W., Wei, Y., Jin, Y., & Zhu, C. (2018) · Measurement · doi:10.1016/j.measurement.2018.09.011 · Cited by 131
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
Gong, S., Xing, K., Cichocki, A., & Li, J. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3079712 · Cited by 135
“Available data size in EEG studies is significantly smaller than that available in image or speech studies [25], [26].”
BackgroundResultA Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases
Allaouzi, I., & Ben Ahmed, M. (2019) · IEEE Access · doi:10.1109/access.2019.2916849 · Cited by 126
“Where the first stage aims to extract hand-crafted features from image using feature descriptors, and then the extracted features are provided as input to a trainable classifier in the second stage [2].”
BackgroundMozafari, M., Ganjtabesh, ., Nowzari-Dalini, A., & Masquelier, T. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00625 · Cited by 125
“In recent years, with advanced techniques based on deep convolutional neural networks (DCNNs) (Rawat and Wang, 2017; Gu et al., 2018), artificial vision has never been closer to human vision.”
BackgroundPandey, D., Nassa, V. K., Pandey, B. K., Thankachan, B., Dadheech, P., Mahajan, D. A., & George, A. S. (2024) · Advances in civil and industrial engineering book series · doi:10.4018/979-8-3693-1335-0.ch003 · Cited by 126
Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study
Mohajerani, Y., Wood, M., Velicogna, I., & Rignot, E. J. (2019) · Remote Sensing · doi:10.3390/rs11010074 · Cited by 133
“We develop a Convolutional Neural Network (CNN) [22] with a U-Net architecture [21] with custom sample weights for the segmentation of glacier fronts.”
MethodInfluentialFERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Wang, Y., Sun, Y., Huang, Y., Liu, Z., Gao, S., Zhang, W., Ge, W., & Zhang, W. (2022) · 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/cvpr52688.2022.02025 · Cited by 130
“Deep CNNs (2D ConvNet) such as VGG [39] and ResNet [19], have made great success on image classification tasks [36].”
BackgroundWang, M. W. H., Goodman, J. M., & Allen, T. E. H. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00316 · Cited by 133
Latent variable models in the era of industrial big data: Extension and beyond
Kong, X., Jiang, X., Zhang, B., Yuan, J., & Ge, Z. (2022) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2022.09.005 · Cited by 128
“Such as convolutional neural networks (CNN) in image recognition [42], recurrent neural networks (RNN) and its variant long short-term memory (LSTM) [43] networks in natural language processing [44, 45], deep generative models like generative adversarial network (GAN) in data generation [46], and deep reinforcement le…”
BackgroundEdge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications
Plastiras, G., Terzi, M., Kyrkou, C., & Theocharides, T. (2018) · 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP) · doi:10.1109/asap.2018.8445118 · Cited by 126
“puter vision tasks, such as large scale image classification [1].”
BackgroundMethodWireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model
Rustam, F., Siddique, M. A., Siddiqui, H. U. R., Ullah, S., Mehmood, A., Ashraf, I., & Choi, G. S. (2021) · IEEE Access · doi:10.1109/access.2021.3061592 · Cited by 128
Deep Learning for Automated Feature Discovery and Classification of Sleep Stages
Sokolovsky, M., Guerrero, F., Paisarnsrisomsuk, S., Ruiz, C., & Álvarez, S. A. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2912955 · Cited by 120
“Convolutional Neural Networks are variants of neural network (NN) statistical learning models and have been successfully applied to image recognition tasks, achieving current state-of-art results in image classification [4], [6], [18].”
BackgroundMethodJamil, S., & Roy, A. M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106734 · Cited by 134
A deep learning model for real-time mortality prediction in critically ill children
Kim, S. Y., Kim, S., Cho, J., Kim, Y. S., Sol, I. S., Sung, Y., Cho, I., Park, M., Jang, H., Kim, Y. H., et al. (2019) · Critical Care · doi:10.1186/s13054-019-2561-z · Cited by 123
Attention GANs: Unsupervised Deep Feature Learning for Aerial Scene Classification
Yu, Y., Li, X., & Liu, F. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2937830 · Cited by 122
“1) Channel Attention Submodule: In order to aggregate abundant information in each channel, we employ maxpooling, L p-pooling [69] and average-pooling to squeeze the spatial information of the input feature map, respectively.”
MethodDeep learning in glaucoma with optical coherence tomography: a review
Ran, A. R., Tham, C. C. Y., Chan, P. P. M., Cheng, C., Tham, Y. C., Rim, T. H., & Cheung, C. Y.-L. (2020) · Eye · doi:10.1038/s41433-020-01191-5 · Cited by 123
“CNNs are feedforward networks that the learning process happens from input images to output classifications only [39].”
BackgroundAngioNet: a convolutional neural network for vessel segmentation in X-ray angiography
Iyer, K., Najarian, C., Fattah, A., Arthurs, C. J., Soroushmehr, S. M. R., Subban, V., Sankardas, M. A., Nadakuditi, R. R., Nallamothu, B. K., & Figueroa, C. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-97355-8 · Cited by 117
Ren, H., Xu, C., Ma, Z., & Sun, Y. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117985 · Cited by 117
Lee, W., Lenferink, A. T. M., Otto, C., & Offerhaus, H. L. (2019) · Journal of Raman Spectroscopy · doi:10.1002/jrs.5770 · Cited by 117
Deep learning enables automated scoring of liver fibrosis stages
Yu, Y., Wang, J., Ng, C. W., Ma, Y., Mo, S., Fong, E. L. S., Xing, J., Song, Z., Xie, Y., Si, K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-34300-2 · Cited by 114
Desta, A. K., Ohira, S., Arai, I., & Fujikawa, K. (2022) · Vehicular Communications · doi:10.1016/j.vehcom.2022.100470 · Cited by 113
Milling chatter detection using scalogram and deep convolutional neural network
Tran, M., Liu, M., & Tran, Q. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04807-7 · Cited by 114
“The deep learning technique can overcome the limitations of conventional machine learning techniques, as mentioned previously [26].”
MethodAutomatic Diabetic Retinopathy Diagnosis Using Adaptive Fine-Tuned Convolutional Neural Network
Saeed, F., Hussain, M., & Aboalsamh, H. (2021) · IEEE Access · doi:10.1109/access.2021.3065273 · Cited by 112
“pre-trained on the ImageNet dataset [29].”
DR-Net: An Improved Network for Building Extraction from High Resolution Remote Sensing Image
Chen, M., Wu, J., Liu, L., Zhao, W., Tian, F., Shen, Q., Zhao, B., & Du, R. (2021) · Remote Sensing · doi:10.3390/rs13020294 · Cited by 110
A review on deep reinforcement learning for fluid mechanics: An update
Viquerat, J., Méliga, P., Larcher, A., & Hachem, E. (2022) · Physics of Fluids · doi:10.1063/5.0128446 · Cited by 115
Convolutional networks and transformers for intelligent road tunnel investigations
Rosso, M. M., Marasco, G., Aiello, S., Aloisio, A., Chiaia, B. M., & Marano, G. C. (2022) · Computers & Structures · doi:10.1016/j.compstruc.2022.106918 · Cited by 115
Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN
Zahoor, M. M., Khan, S. H., Alahmadi, T. J., Alsahfi, T., Mazroa, A. A., Sakr, H. A., Alqahtani, S., Albanyan, A., & Alshemaimri, B. K. (2024) · Biomedicines · doi:10.3390/biomedicines12071395 · Cited by 143
Data-driven science and machine learning methods in laser–plasma physics
Döpp, A., Eberle, C., Howard, S., Irshad, F., Lin, J., & Streeter, M. J. V. (2023) · High Power Laser Science and Engineering · doi:10.1017/hpl.2023.47 · Cited by 119
Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting
Cheng, S. L., Prentice, I. C., Huang, Y., Jin, Y., Guo, Y., & Arcucci, R. (2022) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111302 · Cited by 113
Roy, A. M., & Guha, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106049 · Cited by 115
Image reconstruction through a multimode fiber with a simple neural network architecture
Zhu, C., Chan, E. A., Wang, Y., Peng, W., Guo, R., Zhang, B., Soci, C., & Chong, Y. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-79646-8 · Cited by 106
“CNNs have enjoyed immense recent success in computer vision [24], making it natural to investigate using them for MMF image reconstruction.”
BackgroundWang, Y., Zheng, P., Peng, T., Yang, H., & Zou, J. T. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1581-2 · Cited by 110
Applications of machine vision in pharmaceutical technology: A review
Galata, D. L., Mészáros, L. A., Kállai‐Szabó, N., Szabó, E., Pataki, H., Marosi, G., & Nagy, Z. K. (2021) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2021.105717 · Cited by 105
Lee, H., Seo, B., Koellner, T., & Lautenbach, S. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.08.035 · Cited by 104
“The DCNNs approach is one of the state-of-the-art algorithms in image recognition (Yang and Hospedales, 2015; Guo et al., 2016; Rawat and Wang, 2017).”
BackgroundWeichenthal, S., Hatzopoulou, M., & Brauer, M. D. (2018) · Environment International · doi:10.1016/j.envint.2018.11.042 · Cited by 106
“…image classification starts with the image data (a matrix of pixel values) followed by a series of convolution and pooling layers: representations extracted during this process are fed into a final fully connected layer to output a prediction/class label (LeCun et al., 2015; Rawat & Wang, 2017).”
BackgroundMethodInfluentialDeep learning in electron microscopy
Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 118
Smith, J. D., Reza, M. A., Smith, N. L., Gu, J., Ibrar, M., Crandall, D., & Skrabalak, S. E. (2021) · ACS Nano · doi:10.1021/acsnano.0c08974 · Cited by 102
Machine learning assisted quantum state estimation
Lohani, S., Kirby, B. T., Brodsky, M., Danaci, O., & Glasser, R. T. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab9a21 · Cited by 100
“The general setup of our CNN is depicted in figure 1, which consists of feature mappings, max pooling, and dropout layers [30].”
MethodWeed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks
Peteinatos, G. G., Reichel, P., Karouta, J., Andújar, D., & Gerhards, R. (2020) · Remote Sensing · doi:10.3390/rs12244185 · Cited by 102
“CNNs can provide insights into image related datasets that we have not yet understood, achieving identification accuracies that sometimes surpass the human-level performance [21].”
BackgroundAdvances and Perspectives in Applying Deep Learning for Drug Design and Discovery
Lipinski, C. F., Maltarollo, V. G., Oliveira, P. R., da Silva, A. B. F., & Honório, K. M. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00108 · Cited by 106
Human Gait Analysis Using OpenPose
Viswakumar, A., Rajagopalan, V., Ray, T., & Parimi, C. (2019) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip47207.2019.8985781 · Cited by 101
“Convolutional Neural Networks (CNN) are explicitly designed for the analysis and study of digital images [13].”
BackgroundDraelos, R. L., Dov, D., Mazurowski, M. A., Lo, J. Y.-C., Henao, R., Rubin, G. D., & Carin, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101857 · Cited by 110
A deep learning study on osteosarcoma detection from histological images
Anisuzzaman, D., Barzekar, H., Tong, L., Luo, J., & Yu, Z. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102931 · Cited by 107
“Remarkable progress has been achieved in medical images, primarily due to the availability of large-scale datasets and deep convolutional neural networks (CNNs) in the computer science area [15].”
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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