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 501–600 of 1,000

Emotion Recognition using Facial Expressions in Children using the NAO Robot
Lopez‐Rincon, A. (2019) · International Conference on Electronics, Communications, and Computers · doi:10.1109/conielecomp.2019.8673111 · Cited by 48
“CNNs are used to classify different problems, such as medical data [18], [22], [36], audio [10], and images [12], [33] just to mention some”
Background
Neural hierarchical models of ecological populations
Joseph, M. B. (2020) · Ecology Letters · doi:10.1111/ele.13462 · Cited by 53
Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2
Keenan, T. D. L., Chen, Q., Peng, Y., Domalpally, A., Agrón, E., Hwang, C. K., Thavikulwat, A. T., Lee, D. H., Li, D., Wong, W. T., et al. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.05.036 · Cited by 45
Automated classification of civil structure defects based on convolutional neural network
Savino, P., & Tondolo, F. (2021) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-021-0725-9 · Cited by 48
Auto-adaptive multilayer perceptron for univariate time series classification
Campo, F. A. D., Neri, M. C. G., Villegas, O. O. V., Sánchez, V. G. C., Domínguez, H. D. J. O., & García, V. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115147 · Cited by 45
Detecting visual design principles in art and architecture through deep convolutional neural networks
Demir, G., Çekmiş, A., Yesilkaynak, V. B., & Ünal, G. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103826 · Cited by 49
“They are highly appreciated for their success and progress in image classification tasks and for surpassing human-level performance on several single-label image classification tasks [18].”
How to Measure Grana – Ultrastructural Features of Thylakoid Membranes of Plant Chloroplasts
Mazur, R., Mostowska, A., & Kowalewska, Ł. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.756009 · Cited by 45
An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images
Qader, S. M., Hassan, B. A., & Rashid, T. A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13260-w · Cited by 47
“Using appropriate techniques to tune the hyperparameters of DCNN is significant to improve the classification technique [47]; therefore, the combination of Grey Wolf Optimization and Harris Hawks Optimization is considered to enhance the performance.”
Method
Real-time ore sorting using color and texture analysis
Shatwell, D. G., Murray, V., & Barton, A. (2023) · International Journal of Mining Science and Technology · doi:10.1016/j.ijmst.2023.03.004 · Cited by 48
Detection of Deep Fake Images Using Convolutional Neural Networks
Aggarwal, G., Srivastava, A. K., Jhajharia, K., Sharma, N. V., & Singh, G. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10389973 · Cited by 45
Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning
Azuaje, F. J., Kim, S., Pérez-Hernández, D., & Dittmar, G. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8101535 · Cited by 46
Selective peripheral nerve recordings from nerve cuff electrodes using convolutional neural networks
Koh, R. G. L., Balas, M., Nachman, A., & Zariffa, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab4ac4 · Cited by 49
“emphasize patterns among neighbouring points [50] and are thus able to extract useful structural features from the spatiotemporal signatures in their 2D form, which is lost when the spatiotemporal signatures are represented as 1D vectors.”
Flora Capture: a citizen science application for collecting structured plant observations
Boho, D., Rzanny, M., Wäldchen, J., Nitsche, F., Deggelmann, A., Wittich, H. C., Seeland, M., & Mäder, P. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03920-9 · Cited by 45
A Deep Learning Streaming Methodology for Trajectory Classification
Kontopoulos, I., Makris, A., & Tserpes, K. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10040250 · Cited by 46
“The most commonly used technique regarding computer vision includes convolutional neural networks [11], where each layer of the network identifies a different aspect of the image.”
Method
A dilated inception CNN-LSTM network for fetal heart rate estimation
Fotiadou, E., van Sloun, R. J. G., van Laar, J. O. E. H., & Vullings, R. (2021) · Physiological Measurement · doi:10.1088/1361-6579/abf7db · Cited by 43
Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization
Podgorelec, V., Pečnik, Š., & Vrbančič, G. (2020) · Applied Sciences · doi:10.3390/app10238494 · Cited by 45
Skin Lesion Detection and Classification Using Convolutional Neural Network for Deep Feature Extraction and Support Vector Machine
Aguayo, O. P. Y., Pereira, J. P., Pila‐Varela, K. O., Vásquez, P. A., Veintimilla, K. S., Villalba-Meneses, F., Alvarado-Cando, O., & Almeida-Galárraga, D. A. (2021) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.11.3.13679 · Cited by 44
“However, training deep CNNs from scratch for the classification of images requires a large amount of labeled training data, extensive computational and memory resources, and a great deal of expertise to ensure proper convergence, which results in an extremely time-consuming process [20].”
BackgroundMethod
Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning
Gao, Q., Pan, S., Wang, H., Wei, R., & Wang, J. (2021) · Advances in Aerodynamics · doi:10.1186/s42774-021-00087-6 · Cited by 44
A remaining useful life prediction method based on time–frequency images of the mechanical vibration signals
Du, X., Jia, W., Yu, P., Shi, Y., & Cheng, S. (2022) · Measurement · doi:10.1016/j.measurement.2022.111782 · Cited by 44
Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans
Al‐Tahhan, F. E., Khouqeer, G. A., Saadi, S., Elgarayhi, A., & Sallah, M. (2023) · Diagnostics · doi:10.3390/diagnostics13050864 · Cited by 44
“The input data are fed into four independent layers (1 × 1, 3 × 3, 5 × 5 convolution layers and 3 × 3 max pooling layer), and the outputs are combined into a single dataset [5,8].”
BackgroundMethodInfluential
Neuromorphic Photonics Circuits: Contemporary Review
Kutluyarov, R. V., Zakoyan, A. G., Voronkov, G. S., Grakhova, E. P., & Butt, M. A. (2023) · Nanomaterials · doi:10.3390/nano13243139 · Cited by 45
A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction
Niu, K., Zhang, H., Zhou, T., Cheng, C., & Wang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2902185 · Cited by 45
Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines
Andoga, R., Fözö, L., Schrötter, M., Češkovič, M., Szabó, S., Bréda, R., & Schreiner, M. (2019) · Applied Sciences · doi:10.3390/app9112253 · Cited by 46
Residual Deep Learning System for Mass Segmentation and Classification in Mammography
Abdelhafiz, D., Nabavi, S., Ammar, R. A. A., Yang, C., & Bi, J. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342157 · Cited by 44
“Recent advances in computational technologies, and significant progress in deep learning (DL) [20, 27] and image processing techniques [24] have opened up unprecedented opportunities to develop models for providing an objective view to radiologists with higher accuracy [1, 4, 39].”
Background
Convolutional neural network based obstacle detection for unmanned surface vehicle
Ma, L., Xie, W., & Huang, H. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020045 · Cited by 44
Distracted driving recognition method based on deep convolutional neural network
Rao, X., Lin, F., Chen, Z., & Zhao, J. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01597-4 · Cited by 44
DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning
Abbas, Q., & Ibrahim, M. E. A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09630-x · Cited by 46
White blood cell type identification using multi-layer convolutional features with an extreme-learning machine
Khan, A. H., Eker, A., Chefranov, A. G., & Demirel, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102932 · Cited by 45
Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data
Bourgeais, V., Zehraoui, F., Hamdoune, M. B., & Hanczar, B. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04370-7 · Cited by 49
“Note that this capacity of extracting specific patterns in the first layers and generic patterns in the last layers is well-known in the deep learning models, which has been widely studied in the convolutional neural networks for image analysis [30].”
Background
Deep Learning Approaches for Air-Writing Using Single UWB Radar
Hendy, N., Fayek, H. M., & Al‐Hourani, A. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3172727 · Cited by 45
Automating the assessment of biofouling in images using expert agreement as a gold standard
Mannix, E., Wei, S., Woodham, B. A., Wilkinson, P., & Robinson, A. P. (2021) · Scientific Reports · doi:10.1038/s41598-021-81011-2 · Cited by 48
Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network
Kumar, P. M. A., Maddala, J. B., & Sagayam, K. M. (2021) · IETE Journal of Research · doi:10.1080/03772063.2021.1902868 · Cited by 43
Black-Box Modeling of DC–DC Converters Based on Wavelet Convolutional Neural Networks
Rojas-Dueñas, G., Riba, J., & Moreno‐Eguilaz, M. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3098377 · Cited by 43
Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing
Khokher, M. R., Little, L. R., Tuck, G. N., Smith, D., Qiao, M., Devine, C., O’Neill, H. L., Pogonoski, J. J., Arangio, R., & Wang, D. (2021) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2020-0446 · Cited by 48
Automated prediction of lattice parameters from X-ray powder diffraction patterns
Chitturi, S. R., Ratner, D. F., Walroth, R. C., Thampy, V., Reed, E. J., Dunne, M., Tassone, C. J., & Stone, K. H. (2021) · Journal of Applied Crystallography · doi:10.1107/s1600576721010840 · Cited by 51
Meta-Hashing for Remote Sensing Image Retrieval
Tang, X., Yang, Y., Ma, J., Cheung, Y., Liu, C., Liu, F., Zhang, X., & Jiao, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3136159 · Cited by 46
“Recently, the deep convolutional neural networks (DCNNs) [12]–[14] provide new opportunities for CBRSIR.”
Background
A machine learning approach for the discrimination of theropod and ornithischian dinosaur tracks
Lallensack, J. N., Romilio, A., & Falkingham, P. L. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0588 · Cited by 43
“The artificial neural network learns from its mistakes during multiple training iterations in which the weights are optimized to achieve an increasingly good fit to the data structure [13].”
Background
Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction
Periyasamy, A. P. (2023) · Toxics · doi:10.3390/toxics11070575 · Cited by 50
Leveraging Deep Learning to Strengthen the Cyber-Resilience of Renewable Energy Supply Chains: A Survey
Halgamuge, M. N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3365076 · Cited by 48
Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology
Alahmari, S. S., Goldgof, D. B., Hall, L., Phoulady, H. A., Patel, R. H., & Mouton, P. R. (2018) · Journal of Chemical Neuroanatomy · doi:10.1016/j.jchemneu.2018.12.010 · Cited by 43
Convolutional Neural Networks–Based Model for Automated Sewer Defects Detection and Classification
Zhou, Q., Situ, Z., Teng, S., & Chen, G. (2021) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001394 · Cited by 44
A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges
Arif, M. (2021) · Journal of Information Security and Cybercrimes Research · doi:10.26735/gbtv9013 · Cited by 46
“CapsNet deep neural network [158] with dynamic routing predicts the cyberbullying from the textual content, and ConvNet [159] predicts the cyberbullying from visual contents.”
Background
Piecewise linear neural networks and deep learning
Tao, Q., Li, L., Huang, X., Xi, X., Wang, S., & Suykens, J. A. K. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00125-7 · Cited by 44
Deep Learning Applications in Medical Image Analysis
Singha, A., Thakur, R. S., & Patel, T. (2021) · Wiley eBooks · doi:10.1002/9781119711278.ch11 · Cited by 47
A compressed sensing and CNN‐based method for fault diagnosis of photovoltaic inverters in edge computing scenarios
Wang, X., Yang, B., Wang, Z., Liu, Q., Chen, C., & Guan, X. (2022) · IET Renewable Power Generation · doi:10.1049/rpg2.12383 · Cited by 42
Self-citation
Wear Mechanism Classification Using Artificial Intelligence
Sieberg, P. M., Kurtulan, D., & Hanke, S. (2022) · Materials · doi:10.3390/ma15072358 · Cited by 45
“The focus of the classification method within this contribution is on artificial neural networks, as these are a proven method [19].”
A comprehensive review of methods based on deep learning for diabetes-related foot ulcers
Zhang, J., Qiu, Y., Peng, L. H., Zhou, Q., Wang, Z., & Qi, M. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.945020 · Cited by 49
Self-citation
Phenotyping for QTL identification: A case study of resistance to Plasmopara viticola and Erysiphe necator in grapevine
Possamai, T., & Wiedemann‐Merdinoglu, S. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.930954 · Cited by 42
Unraveling the Effect of the Water Content in the Electrolyte on the Resistive Switching Properties of Self-Assembled One-Dimensional Anodized TiO2 Nanotubes
Nirmal, K. A., Nhivekar, G. S., Khot, A. C., Dongale, T. D., & Kim, T. G. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01075 · Cited by 42
Designing optimal convolutional neural network architecture using differential evolution algorithm
Ghosh, A., Jana, N. D., Mallik, S., & Zhao, Z. (2022) · Patterns · doi:10.1016/j.patter.2022.100567 · Cited by 44
“These layers are stacked to form a workable CNN model.(40) Figure 1 shows a conventional structure of a CNN model with different numbers of Conv, Pool, and FC and one output layer at the end.”
Method
Two Decades of Bengali Handwritten Digit Recognition: A Survey
Rahman, A., Hasan, M. B., Ahmed, S., Ahmed, T., Ashmafee, M. H., Kabir, M. R., & Kabir, M. H. (2022) · IEEE Access · doi:10.1109/access.2022.3202893 · Cited by 48
“Starting with the low-level features produced by the earlier layers, the deeper convolution layers combine them to produce a deeper meaning of the data [165].”
Background
Structural damage severity classification from time-frequency acceleration data using convolutional neural networks
Jamshidi, M., & El‐Badry, M. (2023) · Structures · doi:10.1016/j.istruc.2023.05.009 · Cited by 45
Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines
Ruiz, D., Casas, A., Escobar, C. A., Pérez, A., & Gonzalez, V. (2024) · Sensors · doi:10.3390/s24113564 · Cited by 42
“CNNs, as a type of ANN, are commonly used in computer vision for image classification [40,41], although they can also be applied to regression problems [42].”
Method
Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning
Hafiz, R., Haque, M. R., Rakshit, A., & Uddin, M. S. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.08.015 · Cited by 42
Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning
Abdelmotaal, H., Mostafa, M. M., Mostafa, A. N. R., Mohamed, A.-S. A., & Abdelazeem, K. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.13.30 · Cited by 43
Parkinson’s Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial
Pianpanit, T., Lolak, S., Sawangjai, P., Sudhawiyangkul, T., & Wilaiprasitporn, T. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3077949 · Cited by 47
Internal temperature prediction of ternary polymer lithium‐ion battery pack based on CNN and virtual thermal sensor technology
Wang, M., Hu, W., Jiang, Y., Su, F., & Fang, Z. (2021) · International Journal of Energy Research · doi:10.1002/er.6699 · Cited by 41
“Fully connected layer interprets the extracted abstract features and performs high-level reasoning.(27) Tanh is a nonlinear activation function, and it was taken as the activation function of output layer in this experiment.”
BackgroundMethod
Synthetic datasets for Deep Learning in computer-vision assisted tasks in manufacturing
Manettas, C., Nikolakis, N., & Alexopoulos, K. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.10.038 · Cited by 43
Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops
López-Correa, J. M., Moreno, H., Ribeiro, Á., & Andújar, D. (2022) · Agronomy · doi:10.3390/agronomy12122953 · Cited by 41
BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences
Valeri, J. A., Soenksen, L. R., Collins, K. M., Ramesh, P., Cai, G., Powers, R. K., Angenent-Mari, N. M., Camacho, D. M., Wong, F. J., Lu, T. K., et al. (2023) · Cell Systems · doi:10.1016/j.cels.2023.05.007 · Cited by 40
“Even among skilled ML practitioners, the appropriate selection of algorithmic techniques and tuning of model parameters—typically ranging in number from thousands to hundreds of millions—is difficult.(8,9) Indeed, manual model definition”
Background
C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation
Yi, Y., Jiang, Y., Zhou, B., Zhang, N., Dai, J., Huang, X., Zeng, Q., & Zhou, W. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107215 · Cited by 40
Shallow hybrid quantum-classical convolutional neural network model for image classification
Wang, A., Hu, J., Zhang, S., & Li, L. (2024) · Quantum Information Processing · doi:10.1007/s11128-023-04217-5 · Cited by 43
MFINet: Multi-Scale Feature Interaction Network for Change Detection of High-Resolution Remote Sensing Images
Ren, W., Zhong-chen, W., Xia, M., & Lin, H. (2024) · Remote Sensing · doi:10.3390/rs16071269 · Cited by 41
Cascade refinement extraction network with active boundary loss for segmentation of concrete cracks from high-resolution images
Deng, L., Yuan, H., Long, L., Chun, P., Chen, W., & Chu, H.-H. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105410 · Cited by 41
“In contrast to the “ shallow network ” used in classical ML methods, deep learning (DL)-based methods with a “ deep network ” have been extensively used in both industry and academia owing to their wide adaptability to various real-world scenarios [15].”
Towards Global Explanations of Convolutional Neural Networks With Concept Attribution
Wu, W., Su, Y., Chen, X., Zhao, S., King, I., Lyu, M. R.-T., & Tai, Y. (2020) · 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/cvpr42600.2020.00868 · Cited by 43
“These models cover representative sorts of models for image classification and have wide application in practice [29].”
Background
Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs)
Liu, Y., Yao, L., Xia, Z., Gao, Y., & Gong, Z. (2020) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2020.118973 · Cited by 42
Deconvolution of ultrasonic signals using a convolutional neural network
Chapon, A., Pereira, D., Toews, M., & Bélanger, P. (2020) · Ultrasonics · doi:10.1016/j.ultras.2020.106312 · Cited by 41
Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera
Bai, J., Li, S., Zhang, H., Huang, L., & Wang, P. (2021) · Sensors · doi:10.3390/s21041116 · Cited by 41
“Cameras are the most widely used in intelligent vehicles and ITSs, providing rich scenario information such as target classification [47], lane lines [48], traffic signs [49], etc.”
Background
End-to-end heart sound segmentation using deep convolutional recurrent network
Chen, Y., Sun, Y., Lv, J., Jia, B., & Huang, X. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00325-w · Cited by 41
DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images
Seydi, S. T., Hasanlou, M., & Chanussot, J. (2021) · Remote Sensing · doi:10.3390/rs13245138 · Cited by 41
Hybrid fuzzy multiple SVM classifier through feature fusion based on convolution neural networks and its practical applications
Yang, C., Oh, S., Yang, B., Pedrycz, W., & Wang, L. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117392 · Cited by 41
Application of artificial intelligence techniques for automated detection of myocardial infarction: a review
Joloudari, J. H., Mojrian, S., Nodehi, I., Mashmool, A., kiani-Zadegan, Z., Shirkharkolaie, S. K., Alizadehsani, R., Tamadon, T., Khosravi, S., Kohnehshari, M. A., et al. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac7fd9 · Cited by 43
“DL models usually yield excellent performance for detecting and classifying early changes in the disease course [23-31].”
Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea
Nikitin, N. O., Revin, I., Hvatov, A., Vychuzhanin, P., & Kalyuzhnaya, A. V. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105061 · Cited by 40
“The practical approach for such a problem is the application of the convolutional neural networks [30].”
Method
Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network
Alanazi, F., Jambi, K. M., Eassa, F. E., Khemakhem, M. A., Basuhail, A. A., & Alsubhi, K. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.024668 · Cited by 40
Hybrid Optimization-Enabled Deep Learning for Indoor Object Detection and Distance Estimation to Assist Visually Impaired Persons
Nagarajan, A., & Gopinath, M. P. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103362 · Cited by 40
Quantitative analysis of aflatoxin B1 in moldy peanuts based on near-infrared spectra with two-dimensional convolutional neural network
Jiang, H., Deng, J., & Zhu, C. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104672 · Cited by 41
Ensemble Federated Learning: An approach for collaborative pneumonia diagnosis
Mabrouk, A., Redondo, R. P. D., Elaziz, M. A., & Kayed, M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110500 · Cited by 43
“[16].”
Classification of oolong tea varieties based on computer vision and convolutional neural networks
Zhu, Y., Chen, S., Yin, H., Han, X., Xu, M., Wang, W., Zhang, Y., Feng, X., & Liu, Y. (2023) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.13049 · Cited by 46
Application of artificial intelligence in reverse logistics: A bibliometric and network analysis
Bhowmik, O., Chowdhury, S., Ashik, J. H., Mahmud, G., Khan, M. M., & Hossain, N. U. I. (2024) · Supply Chain Analytics · doi:10.1016/j.sca.2024.100076 · Cited by 60
An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images
Zubair, M., Owais, M., Hassan, T., Bendechache, M., Hussain, M., Hussain, I., & Werghi, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-97256-0 · Cited by 61
“The VGG model has a lot of benefits 137 .”
Nonlocal phase-change metaoptics for reconfigurable nonvolatile image processing
Yang, G., Wang, M., Lee, J. S., Farmakidis, N., Shields, J., de Galarreta, C. R., Kendall, S., Bertolotti, J., Moskalenko, A., Huang, K., et al. (2025) · Light Science & Applications · doi:10.1038/s41377-025-01841-x · Cited by 46
Arabic (Indian) digit handwritten recognition using recurrent transfer deep architecture
Alkhawaldeh, R. S. (2020) · Soft Computing · doi:10.1007/s00500-020-05368-8 · Cited by 42
Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks
Chew, A. K., Jiang, S., Zhang, W., Zavala, V. M., & Lehn, R. C. V. (2020) · Chemical Science · doi:10.1039/d0sc03261a · Cited by 40
Predicting the fluid behavior of random microfluidic mixers using convolutional neural networks
Wang, J., Zhang, N., Chen, J., Su, G., Yao, H., Ho, T.-Y., & Sun, L. (2020) · Lab on a Chip · doi:10.1039/d0lc01158d · Cited by 40
A Survey on Automatic Inspections of Overhead Contact Lines by Computer Vision
Yu, L., Gao, S., Zhang, D., Kang, G., Zhan, D., & Roberts, C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3119023 · Cited by 43
Core Challenges in Embodied Vision-Language Planning
Francis, J., Kitamura, N., Labelle, F., Lu, X., Navarro, I., & Oh, J. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13646 · Cited by 44
Advanced Thermal Fluid Leakage Detection System with Machine Learning Algorithm for Pipe-in-Pipe Structure
Kim, H., Lee, J., Kim, T., Park, S. J., Kim, H., & Jung, I. D. (2022) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2023.102747 · Cited by 39
Kinetics-constrained neural ordinary differential equations: Artificial neural network models tailored for small data to boost kinetic model development
Fedorov, A. V., Perechodjuk, A., & Linke, D. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.146869 · Cited by 40
AI-assisted proton exchange membrane (PEM) fuel cell performance prediction using CFD and data-driven surrogate models
Siddiqa, S., Chang, K., Naqvi, S. B., Azam, M., Molla, M. M., & Nguyen, K.-H. (2024) · International Communications in Heat and Mass Transfer · doi:10.1016/j.icheatmasstransfer.2024.107616 · Cited by 39
Deep learning in material recovery: Development of method to create training database
Vrancken, C., Longhurst, P., & Wagland, S. T. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.077 · Cited by 44
Apparel-based deep learning system design for apparel style recommendation
Guan, C., Qin, S., & Long, Y. (2019) · International Journal of Clothing Science and Technology · doi:10.1108/ijcst-02-2018-0019 · Cited by 44
“Rawat and Wang summarised the remarkable works of CNN from a single label, multi-label to semantic classification problems (Rawat and Wang, 2017).”
Background
Detecting microseismic events in downhole distributed acoustic sensing data using convolutional neural networks
Binder, G., & Chakraborty, D. (2019) · Expanded abstracts with biographies/SEG technical program expanded abstracts · doi:10.1190/segam2019-3214863.1 · Cited by 39
Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library
Matsuzaka, Y., & Uesawa, Y. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20194855 · Cited by 40
Hyperspectral Image Classification With Pre-Activation Residual Attention Network
Gao, H., Yang, Y., Yao, D., & Li, C. (2019) · IEEE Access · doi:10.1109/access.2019.2957163 · Cited by 39
“In recent years, deep learning has attracted increased attention for its remarkable performance in many fields, such as image classification [19], [20], target detection [21], [22], and natural language processing [23].”
Background
The Unbearable Shallow Understanding of Deep Learning
Plebe, A., & Grasso, G. M. (2019) · Minds and Machines · doi:10.1007/s11023-019-09512-8 · Cited by 43
Skill transfer support model based on deep learning
Wang, K., Rizqi, D. A., & Nguyen, H.-P. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01606-w · Cited by 40
“Convolutional neural network (CNN) architecture successfully outperforms other deep learning models for most image recognition, classification, and detection tasks (Rawat and Wang 2017).”
Background
Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array
Binder, G., & Tura, A. (2020) · Geophysical Prospecting · doi:10.1111/1365-2478.13027 · Cited by 39
Integrating domain knowledge with deep learning models: An interpretable AI system for automatic work progress identification of NATM tunnels
Wu, R., Fujita, Y., & Soga, K. (2020) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2020.103558 · Cited by 40
Prediction of tire pattern noise in early design stage based on convolutional neural network
Lee, S., Lee, H., Back, J., An, K., Yoon, Y., Yum, K., kim, S., & Hwang, S.-U. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107617 · Cited by 42
Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning
Zhang, F., Wu, S., Liu, J., Wang, C., Guo, Z., Xu, A., Pan, K., & Pan, X. (2020) · Soil Science Society of America Journal · doi:10.1002/saj2.20193 · Cited by 40

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