Cited by 3,503Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

The DOI matches its Crossref record; 274 of the 286 references it cites were found in a registry or an index.

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, Waseem, Wang, Zenghui·2017·Neural Computation

DOI 10.1162/neco_a_00990

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1162/neco_a_00990

Cites

274 of 274 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
Long Short-Term Memory
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
Deep learning
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

Show all 286 references

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

Sorted by citations

Items 401–500 of 1,000

Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling
Cheng, S. L., Yufang Jin, A., Harrison, S. P., Quilodrán-Casas, C., Prentice, I. C., Guo, Y., & Arcucci, R. (2022) · Remote Sensing · doi:10.3390/rs14133228 · Cited by 57
“Neural Network (CNN) and CAE, interested readers are referred to [51].”
Background
Interpretation and Classification of Arrhythmia Using Deep Convolutional Network
Singh, P., & Sharma, A. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3204316 · Cited by 62
“Recent achievements by deep neural networks (DNNs) in 62 computer vision [5], image classification [6], speech recogni- 63 tion, and natural language processing [7] have been impres- 64 sive, as these networks are expected to have a significant 65 impact on the field of health care and clinical practice.”
Background
Time-series based prediction for energy consumption of smart home data using hybrid convolution-recurrent neural network
Bhoj, N., & Bhadoria, R. S. (2022) · Telematics and Informatics · doi:10.1016/j.tele.2022.101907 · Cited by 61
Using Deep Learning to Identify Molecular Junction Characteristics
Fu, T., Zang, Y., Zou, Q., Nuckolls, C. P., & Venkataraman, L. (2020) · Nano Letters · doi:10.1021/acs.nanolett.0c00198 · Cited by 57
Detection of early stages of Alzheimer’s disease based on MEG activity with a randomized convolutional neural network
López‐Martín, M., Nevado, Á., & Carro, B. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101924 · Cited by 57
“- 2-D CNN: A classic 2-D CNN [19] applied to synthetic images formed by an arbitrary arrangement of the features obtained from the MEG signals.”
Method
Deep learning in breast radiology: current progress and future directions
Ou, W. C., Polat, D. S., & Doğan, B. E. (2021) · European Radiology · doi:10.1007/s00330-020-07640-9 · Cited by 57
Multi-view Convolutional Recurrent Neural Networks for Lung Cancer Nodule Identification
Abid, M. M. N., Zia, T., Ghafoor, M., & Windridge, D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.06.144 · Cited by 57
“Convolution is followed by maxpooling which selects the maximum value within a receptive field [30, 31].”
Background
Unraveling ML Models of Emotion With NOVA: Multi-Level Explainable AI for Non-Experts
Heimerl, A., Weitz, K., Baur, T., & André, E. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3043603 · Cited by 64
“CNNs set the benchmark on various famous image classification challenges like MNIST and ImageNet Large Scale Visual Recognition Challenge [42].”
Background
A cost‐effective maize ear phenotyping platform enables rapid categorization and quantification of kernels
Warman, C., Sullivan, C. M., Preece, J., Buchanan, M. E., Vejlupkova, Z., Jaiswal, P., & Fowler, J. E. (2021) · The Plant Journal · doi:10.1111/tpj.15166 · Cited by 56
SHM under varying environmental conditions: an approach based on model order reduction and deep learning
Torzoni, M., Rosafalco, L., Manzoni, A., Mariani, S., & Corigliano, A. (2022) · Computers & Structures · doi:10.1016/j.compstruc.2022.106790 · Cited by 56
Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey
Kharsa, R., Bouridane, A., & Amira, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126843 · Cited by 66
Single-cell barcode analysis provides a rapid readout of cellular signaling pathways in clinical specimens
Giedt, R. J., Pathania, D., Carlson, J., McFarland, P. J., del Castillo, A. F., Juric, D., & Weissleder, R. (2018) · Nature Communications · doi:10.1038/s41467-018-07002-6 · Cited by 54
Structural damage-causing concrete cracking detection based on a deep-learning method
Han, X., Zhao, Z., Chen, L., Hu, X., Tian, Y., Zhai, C., Wang, L., & Huang, X. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.127562 · Cited by 55
ECG-Based Driver’s Stress Detection Using Deep Transfer Learning and Fuzzy Logic Approaches
Amin, M., Ullah, K., Asif, M., Waheed, A., Haq, S. U., Zareei, M., & Biswal, R. R. (2022) · IEEE Access · doi:10.1109/access.2022.3158658 · Cited by 58
An Improved U-Net Image Segmentation Method and Its Application for Metallic Grain Size Statistics
Shi, P., Duan, M., Yang, L., Feng, W., Ding, L., & Jiang, L.-W. (2022) · Materials · doi:10.3390/ma15134417 · Cited by 44
“The Deeplab method combines deep convolutional neural networks (DCNNs [32]) with a probability graph model (DenseCRFs [33]), which can complete per-pixel classification and achieve good image segmentation results.”
Method
Detection of whole body bone fractures based on improved YOLOv7
Zou, J., & Arshad, M. R. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.105995 · Cited by 61
A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture
Anam, I., Arafat, N., Hafiz, M. S., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100647 · Cited by 72
A Comparative Analysis on Fruit Freshness Classification
Ulucan, D., Ulucan, O., & Türkan, M. (2019) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu48272.2019.8946385 · Cited by 55
“Classification of images, object recognition and digital character recognition are some examples of fields where CNNs have already been adopted successfully [20].”
Background
A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic
Rahman, M. M., Khatun, F., Uzzaman, A., Sami, S. I., Bhuiyan, M. A.-A., & Kiong, T. S. (2021) · International Journal of Health Services · doi:10.1177/00207314211017469 · Cited by 59
Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks
Molina, Á., Rodellar, J., Boldú, L., Acevedo, A., Alférez, S., & Merino, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104680 · Cited by 54
A systematic survey of deep learning in breast cancer
Yu, X., Zhou, Q., Wang‎, S., & Zhang, Y. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22622 · Cited by 57
“In feature extraction, handcrafted features are firstly extracted for the following trainable classifiers.(84) However, a major shortcoming is that the systems' performance heavily relies on the design of feature extraction.”
Background
Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images
Murcia-Gómez, D., Rojas, I., & Valenzuela, O. (2022) · Applied Sciences · doi:10.3390/app122211375 · Cited by 58
A survey of deep learning applications in cryptocurrency
Zhang, J., Cai, K., & Wen, J. (2023) · iScience · doi:10.1016/j.isci.2023.108509 · Cited by 68
Identifying Reionization Sources from 21cm Maps using Convolutional Neural Networks
Hassan, S., Liu, A., Kohn, S. A., & Plante, P. L. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3282 · Cited by 54
“CNNs are a class of deep learning models that are powerful for large scale image recognition (for a comprehensive review see Rawat & Wang 2017).”
Background
Competitive residual neural network for image classification
Hanif, M. S., & Bilal, M. (2019) · ICT Express · doi:10.1016/j.icte.2019.06.001 · Cited by 54
A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew
Bierman, A., LaPlumm, T., Cadle-Davidson, L. E., Gadoury, D. M., Martínez, D., Sapkota, S., & Rea, M. S. (2019) · Plant Phenomics · doi:10.34133/2019/9209727 · Cited by 54
“For a description of CNNs and recent advances inmachine vision the reader is directed to review articles on this topic [8, 9].”
Background
Identification, classification, and quantification of three physical mechanisms in oil-in-water emulsions using AlexNet with transfer learning
Lü, T., Yu, F., Xue, C., & Han, B. (2020) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2020.110220 · Cited by 54
Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning
Aguénounon, E., Smith, J. T., Al‐Taher, M., Diana, M., Intes, X., & Gioux, S. (2020) · Biomedical Optics Express · doi:10.1364/boe.397681 · Cited by 54
Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning
Benhammou, Y., Alcaraz‐Segura, D., Guirado, E., Khaldi, R., Achchab, B., Herrera, F., & Tabik, S. (2022) · Scientific Data · doi:10.1038/s41597-022-01775-8 · Cited by 60
A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video
Awais, M. A., Long, X., Yin, B., Abbasi, S. F., Akbarzadeh, S., Lu, C., Wang, X., Wang, L., Zhang, J., Dudink, J., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3073632 · Cited by 54
“1) DCNN: DCNN is a multi-layer neural network with a supervised learning architecture that can be viewed as the composition of a trainable classifier and an automatic feature extractor [46].”
Background
Estimating Rainfall Intensity Using an Image-Based Deep Learning Model
Yin, H., Zheng, F., Duan, H., Savic, D. A., & Kapelan, Z. S. (2022) · Engineering · doi:10.1016/j.eng.2021.11.021 · Cited by 57
Luojia-HSSR: A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet
Xu, Y., Gong, J., Huang, X., Hu, X., Li, J., Li, Q., & Peng, M. (2022) · Geo-spatial Information Science · doi:10.1080/10095020.2022.2070555 · Cited by 53
“…improved the feature extraction effectiveness from high spatial resolution remote sensing images(Gong 2018; Grekousis, Mountrakis, and Kavouras 2015; Rawat and Wang 2017; Zhang, Zhang, and Du 2016; Scheirer et al. 2012; Hu, Gong, and Zhang 2018; Gong et al. 2021; Heipke and Rottensteiner 2020).”
Background
Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods
Zhou, W., Li, H., & Anastasio, M. A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2911211 · Cited by 55
“A comprehensive review of CNNs for image classifications can be found in [35].”
Background
Machine learning \& artificial intelligence in the quantum domainPreprint
Dunjko, V., & Briegel, H. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02779 · Cited by 54
Boosting Nonnegative Matrix Factorization Based Community Detection With Graph Attention Auto-Encoder
He, C., Zheng, Y., Fei, X., Li, H., Hu, Z., & Tang, Y. Z. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2021.3103213 · Cited by 52
“In recent years, Deep Neural Networks (DNNs) have revolutionized many machine learning tasks, including image classification [37], speech recognition [38] and machine translation [39].”
Background
A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing
Zheng, H., Sherazi, S. W. A., Son, S. H., & Lee, J. Y. (2021) · Applied Sciences · doi:10.3390/app11209769 · Cited by 52
Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case
Giordano, D., Giobergia, F., Pastor, E., Macchia, A. L., Cerquitelli, T., Baralis, E., Mellia, M., & Tricarico, D. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103554 · Cited by 63
A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves
Jung, D.-H., Kim, J., Kim, H., Lee, T. S., Kim, H. S., & Park, S. H. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.837020 · Cited by 52
“…surpasses conventional image processing techniques in object Frontiers in Plant Science | www.frontiersin.org 4 March 2022 | Volume 13 | Article 837020 FIGURE 5 | Overview of CNN model usage after converting 3D data to 2D data. recognition and classification of RGB images (Rawat and Wang, 2017).”
Method
Graphene-supported ordered mesoporous composites used for environmental remediation: A review
Wu, X., Hu, J., Qi, J., Hou, Y., & Wei, X. (2020) · Separation and Purification Technology · doi:10.1016/j.seppur.2020.116511 · Cited by 52
Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks
Griffiths, J. P., Kleinegesse, S., Saunders, D., Taylor, R., & Vacheret, A. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abb781 · Cited by 63
“Further explanation of CNNs can be found in [22].”
Background
Predicting wind flow around buildings using deep learning
Kim, B., Lee, D., Preethaa, K. R. S., Hu, G., Natarajan, Y., & Kwok, K. C. S. (2021) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2021.104820 · Cited by 54
Indexing of electron back-scatter diffraction patterns using a convolutional neural network
Ding, Z., Pascal, E., & Graef, M. D. (2020) · Acta Materialia · doi:10.1016/j.actamat.2020.08.046 · Cited by 52
Current status of artificial intelligence analysis for endoscopic ultrasonography
Kuwahara, T., Hara, K., Mizuno, N., Haba, S., Okuno, N., Koda, H., Miyano, A., & Fumihara, D. (2020) · Digestive Endoscopy · doi:10.1111/den.13880 · Cited by 54
Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality
Cipollari, S., Guarrasi, V., Pecoraro, M., Bicchetti, M., Messina, E., Farina, L., Paci, P., Catalano, C., & Panebianco, V. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27879 · Cited by 55
“Once the network is trained, it is ready to have the kernels perform convolutions, extracting features to conclude with the desired class label.(19) Despite the high performance of this technology in image analysis, only a few studies have investigated the use of deep neural networks to classify images based on their…”
Background
Deep Learning in Agriculture: A Review
Bharman, P., Saad, S. A., Khan, S., Jahan, I., Ray, M., & Biswas, M. (2022) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2022/v13i230311 · Cited by 59
“It has been widely and effectively utilized in computer vision [100] tasks such as object localization, detection [101], and image classification [102].”
Method
Machine learning algorithms in microbial classification: a comparative analysis
Wu, Y., & Gadsden, S. A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1200994 · Cited by 55
A comprehensive survey on load forecasting hybrid models: Navigating the Futuristic demand response patterns through experts and intelligent systems
Fida, K., Abbasi, U., Adnan, M., Iqbal, S., & Mohamed, S. E. G. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102773 · Cited by 59
Deep Learning Model for Multimedia Quality of Experience Prediction Based on Network Flow Packets
López‐Martín, M., Carro, B., Lloret, J., Gómez, S. E., & Sánchez-Esguevillas, A. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701156 · Cited by 53
Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma
Lu, L., & Daigle, B. J. (2020) · PeerJ · doi:10.7717/peerj.8668 · Cited by 52
Automated plankton classification from holographic imagery with deep convolutional neural networks
Guo, B., Nyman, L., Nayak, A. R., Milmore, D., McFarland, M., Twardowski, M. S., Sullivan, J. M., Yu, J., & Hong, J. (2020) · Limnology and Oceanography Methods · doi:10.1002/lom3.10402 · Cited by 60
“These computational limitations can potentially be addressed with the newly emerged deep learning approach which has been demonstrated as a powerful tool for image processing (Guo et al. 2016; Rawat and Wang 2017).”
Background
VAE-TALSTM: a temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction
Shu, X., Bao, T., Li, Y., Gong, J., & Zhang, K. (2021) · Engineering With Computers · doi:10.1007/s00366-021-01362-2 · Cited by 51
An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management
Ntakolia, C., Kokkotis, C., Karlsson, P., & Moustakidis, S. (2021) · Sensors · doi:10.3390/s21237926 · Cited by 67
“The comparative evaluation included 8 popular and commonly used classifiers, such as Random Forest (RF) [34], K-Nearest Neighbor (KNN) [35], Neural Networks (NN) [36], Logistic Regression (LR) [37], Balanced Blagging (BB) [38], Support Vector Machines (SVM) [39], XGBoost [40], and LightGBM [41].”
Method
In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications
Liu, J., Kuang, W., Liu, J., Gao, Z., Rohani, S., & Gong, J. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.135554 · Cited by 50
A Convolutional Neural Network-Based Surrogate Model for Multi-objective Optimization Evolutionary Algorithm Based on Decomposition
Zhang, T., Li, F., Zhao, X. W., Wang, Q., & Liu, T. (2022) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2022.101081 · Cited by 50
Foundations of machine learning for low-temperature plasmas: methods and case studies
Bonzanini, A. D., Shao, K., Graves, D. B., Hamaguchi, S., & Mesbah, A. (2023) · Plasma Sources Science and Technology · doi:10.1088/1361-6595/acb28c · Cited by 55
“For classification problems involving more than two classes, we can use the softmax operator as the final activation function, which yields the classification probabilities for each class [78, 86, 92].”
Polarimetric SAR Terrain Classification Using 3D Convolutional Neural Network
Zhang, L., Chen, Z., Zou, B., & Gao, Y. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519557 · Cited by 50
“Convolutional neural network is an efficient recognition and classification method and it has been widely used in many fields [4].”
Method
OSVFuseNet: Online Signature Verification by feature fusion and depth-wise separable convolution based deep learning
Vorugunti, C. S., Pulabaigari, V., Gorthi, R. K., & Mukherjee, P. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.072 · Cited by 52
Convolutional neural network and impedance-based SHM applied to damage detection
de Rezende, S. W. F., de Moura, J. D. R. V., Neto, R. M. F., Gallo, C. A., & Steffen, V. (2020) · Engineering Research Express · doi:10.1088/2631-8695/abb568 · Cited by 50
Recent evolutions of machine learning applications in clinical laboratory medicine
Bruyne, S. D., Speeckaert, M. M., Biesen, W. V., & Delanghe, J. (2020) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2020.1828811 · Cited by 53
“The convolutional layer is the layer where most of the computational work is done and serves as a feature extractor [25,26].”
Background
Vertex and energy reconstruction in JUNO with machine learning methods
Qian, Z., Belavin, V., Bokov, V., Brugnera, R., Compagnucci, A., Gavrikov, A., Garfagnini, A., Gonchar, M., Khatbullina, L., Li, Z., et al. (2021) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2021.165527 · Cited by 59
“3 Planar CNN Models Convolutional Neural Networks (CNNs) is a deep learning network class commonly applied to analyzing visual imagery [40].”
Background
AND Flash Array Based on Charge Trap Flash for Implementation of Convolutional Neural Networks
Choi, H.-S., Kim, H., Lee, J., Park, B., & Kim, Y. (2020) · IEEE Electron Device Letters · doi:10.1109/led.2020.3025587 · Cited by 49
“Currently, various hardware frameworks such as graphic processing units (GPUs) [4], field-programmable gate arrays (FGPAs) [5], and application specific integrated circuits (ASICs) [6] are used to implement ANNs.”
Method
Development of a high-throughput plant disease symptom severity assessment tool using machine learning image analysis and integrated geolocation
Clohessy, J. W., Sanjel, S., O’Brien, G. K., Barocco, R. L., Kumar, S., Adkins, S. T., Tillman, B. L., Wright, D. L., & Small, I. M. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106089 · Cited by 49
FishResNet: Automatic Fish Classification Approach in Underwater Scenario
Mathur, M., & Goel, N. (2021) · SN Computer Science · doi:10.1007/s42979-021-00614-8 · Cited by 54
“Transfer learning is a machine learning technique which builds accurate deep learning models in a timesaving way [28].”
Background
A hybrid prototype selection-based deep learning approach for anomaly detection in industrial machines
Monteiro, R. D. P., Cerrada, M., Cabrera, D. R., Sánchez, R., & Bastos-Filho, C. J. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117528 · Cited by 49
Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy
Wang, M., Wang, J., Cui, Y., Liu, J., & Chen, L. (2022) · Agronomy · doi:10.3390/agronomy12102342 · Cited by 62
“In recent years, deep learning models, such as convolutional neural networks (CNNs), have drawn enormous attention in computer vision tasks, including image classification [17,18] and semantic segmentation [19,20].”
Background
HHF: Hashing-Guided Hinge Function for Deep Hashing Retrieval
Xu, C., Chai, Z., Xu, Z., Li, H., Zuo, Q., Yang, L., & Yuan, C. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3222598 · Cited by 49
Convolutional neural network pruning based on multi-objective feature map selection for image classification
Jiang, P., Xue, Y., & Neri, F. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110229 · Cited by 50
A survey on multi-modal social event detection
Zhou, H., Yin, H., Zheng, H., & Li, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105695 · Cited by 49
Footstep detection in urban seismic data with a convolutional neural network
Jakkampudi, S., Shen, J., Li, W., Dev, A., Zhu, T., & Martin, E. (2020) · The Leading Edge · doi:10.1190/tle39090654.1 · Cited by 49
“Numerous researchers have applied the CNN approach to extract subtle spatiotemporal patterns (Rawat and Wang, 2017).”
Classification of rotator cuff tears in ultrasound images using deep learning models
Ho, T. T., Kim, G.-T., Kim, T., Choi, S., & Park, E. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02502-6 · Cited by 49
“Transfer learning is widely used in the field of DL, because it allows to utilize the advantage of deep networks and develop an accurate model in a short time with comparatively little data [37].”
Background
Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
Miloshevich, G., Cozian, B., Abry, P., Borgnat, P., & Bouchet, F. (2023) · Physical Review Fluids · doi:10.1103/physrevfluids.8.040501 · Cited by 53
Deep Convolutional Neural Network for Image Classification on CUDA Platform
Gavali, P. R., & Banu, J. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816718-2.00013-0 · Cited by 48
Efficient Deep Learning-Based Data-Centric Approach for Autism Spectrum Disorder Diagnosis from Facial Images Using Explainable AI
Alam, M. S., Rashid, M. M., Faizabadi, A. R., Zaki, H. F. M., Alam, T. E., Ali, M. S., Gupta, K. D., & Ahsan, M. M. (2023) · Technologies · doi:10.3390/technologies11050115 · Cited by 57
Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks
Meena, G., Mohbey, K. K., & Kumar, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18437-z · Cited by 47
“However, in the case of a limited dataset distinct from the pre-trained model dataset, it is necessary to train just a select number of layers while keeping the remaining levels frozen [53].”
Research progress in water quality prediction based on deep learning technology: a review
Li, W., Zhao, Y., Zhu, Y., Dong, Z., Wang, F., & Huang, F. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-33058-7 · Cited by 53
“…wide-ranging applications in big data analytics, including but not limited to computer vision (Badrinarayanan et al. 2015), image classification (Rawat and Wang 2017), speech recognition (Zhang et al. 2020), time series prediction (Sezer et al. 2019), natural language processing (Otter et al.…”
Detection of a SARS-CoV-2 sequence with genosensors using data analysis based on information visualization and machine learning techniques
Soares, J. C., Soares, A. C., Rodrigues, V. C., Oiticica, P. R. A., Raymundo‐Pereira, P. A., Bott‐Neto, J. L., Buscaglia, L. A., de Castro, L. D. C., Ribas, L. C., Scabini, L. F. D. S., et al. (2021) · Materials Chemistry Frontiers · doi:10.1039/d1qm00665g · Cited by 49
Deep convolutional neural network for 3D mineral identification and liberation analysis
Tang, K., Wang, Y. D., Mostaghimi, P., Knackstedt, M. A., Hargrave, C. O., & Armstrong, R. T. (2022) · Minerals Engineering · doi:10.1016/j.mineng.2022.107592 · Cited by 47
Learning finite element convergence with the Multi-fidelity Graph Neural Network
Black, N., & Najafi, A. R. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.115120 · Cited by 51
Energy- and Area-Efficient CMOS Synapse and Neuron for Spiking Neural Networks With STDP Learning
Joo, B., Han, J., & Kong, B. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3178989 · Cited by 50
Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals
Afzali, A., Khaleghi, A., Hatef, B., Movahed, R. A., & Jahromi, G. P. (2023) · Waves in Random and Complex Media · doi:10.1080/17455030.2023.2187237 · Cited by 48
A multi-view-CNN framework for deep representation learning in image classification
Pintelas, E. G., Livieris, I. E., Kotsiantis, S. B., & Pintelas, P. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103687 · Cited by 47
Proceeding the categorization of microplastics through deep learning-based image segmentation
Huang, H. F., Cai, H., Qureshi, J. U., Mehdi, S. R., Song, H., Liu, C., Di, Y., Shi, H., Yao, W., & Sun, Z. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165308 · Cited by 49
Intelligent vegetable freshness monitoring system developed by integrating eco-friendly fluorescent sensor arrays with deep convolutional neural networks
Wang, D., Zhang, M., Zhu, Q., & Adhikari, B. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.150739 · Cited by 47
Ultrasensitive dim-light neuromorphic vision sensing via momentum-conserved reconfigurable van der Waals heterostructure
Xu, L., Liu, J., Guo, X., Liu, S., Lai, X., Wang, J., Yu, M., Xie, Z., Peng, H., Zou, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53268-4 · Cited by 50
Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks
Lü, C., Wang, B., Jiang, X., Zhang, J., Niu, K., & Yuan, Y. (2018) · Plasma Science and Technology · doi:10.1088/2058-6272/aaef6e · Cited by 46
Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis
Matsuzaka, Y., & Uesawa, Y. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00065 · Cited by 48
“Since AlexNet has 60 million parameters, their optimization was essential to avoid overfitting (Figure 3; Krizhevsky et al., 2012; Szegedy et al., 2014; Cagli et al., 2017; Rawat and Wang, 2017; Aggarwal et al., 2018; Ha et al., 2018; Vakli et al., 2018).”
Background
A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG
Zhang, Q., Fu, L., & Linyue, G. (2019) · Computational and Mathematical Methods in Medicine · doi:10.1155/2019/7095137 · Cited by 48
Deep learning based HEp-2 image classification: A comprehensive review
Rahman, S., Wang, L., Sun, C., & Zhou, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101764 · Cited by 51
Deep thermal imaging to compute the adulteration state of extra virgin olive oil
Izquierdo, M., Lastra-Mejías, M., González-Flores, E., Cancilla, J. C., Aroca-Santos, R., & Torrecilla, J. S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105290 · Cited by 47
In-Air Continuous Writing Using UWB Impulse Radar Sensors
Khan, F., Leem, S. K., & Cho, S. H. (2020) · IEEE Access · doi:10.1109/access.2020.2994281 · Cited by 47
“CNNs are extensively used as a deep learning technique that mimics the human vision system [26].”
Background
Automating Configuration of Convolutional Neural Network Hyperparameters Using Genetic Algorithm
Johnson, F., Valderrama, A., Valle, C., Crawford, B., Soto, R., & Ñanculef, R. (2020) · IEEE Access · doi:10.1109/access.2020.3019245 · Cited by 47
“vision in general [26], [33], even attaining human-level performances in some problems [18].”
Background
Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency
Yi, J., Kang, H. K., Kwon, J.-H., Kim, K.-S., Park, M. H., Seong, Y. K., Kim, D. W., Ahn, B. E., Ha, K., Lee, J.-Y., et al. (2020) · ULTRASONOGRAPHY · doi:10.14366/usg.20102 · Cited by 49
“The issue of small data sets can be alleviated by using transfer learning [44] where, as shown in Fig.”
Method
Detecting stranded macro-litter categories on drone orthophoto by a multi-class Neural Network
Pinto, L., Andriolo, U., & Gonçalves, G. R. (2021) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2021.112594 · Cited by 47
MXene‐Based Broadband Ultrafast Nonlinear Activator for Optical Computing
Zhan, Y., Tan, W., Zhang, T., Chen, C., Wang, Z., Mao, Y. X., Ma, C., Lin, Q., Bi, W., Yu, F., et al. (2022) · Advanced Optical Materials · doi:10.1002/adom.202200714 · Cited by 45
Self-citation
Compact Convolutional Neural Network with Multi-Headed Attention Mechanism for Seizure Prediction
Ding, X., Nie, W., Liu, X., Wang, X., & Yuan, Q. (2023) · International Journal of Neural Systems · doi:10.1142/s0129065723500144 · Cited by 46
Deep learning model of convolutional neural networks powered by a genetic algorithm for prevention of traffic accidents severity
Pérez-Sala, L., Curado, M., Tortosa, L., & Vicent, J. F. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113245 · Cited by 51
A multimodal turn in Digital Humanities. Using contrastive machine learning models to explore, enrich, and analyze digital visual historical collections
Smits, T., & Wevers, M. (2023) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqad008 · Cited by 46
Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification
Zeng, G., Ma, Y., Du, M., Chen, T., Lin, L., Dai, M., Luo, H., Hu, L., Zhou, Q., & Pan, X. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169623 · Cited by 48
Deep learning-based classification, detection, and segmentation of tomato leaf diseases: A state-of-the-art review
Das, A., Pathan, F., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2025) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2025.02.006 · Cited by 66
A comparative study of RNN, CNN, LSTM and GRU for random sea wave forecasting
Jin, X., Dai, C., Tao, Y., & Zhang, F. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.122013 · Cited by 51

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
DOI: 10.1162/neco_a_00990
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks