The DOI matches its Crossref record; 107 of the 109 references it cites were found in a registry or an index.
The state of the art of deep learning models in medical science and their challenges
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“The intelligent models of machine learning and deep learning are attracting much attention and are proving to be very practical [130,131] in the healthcare industry.”
From Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review (2023) · cites this paper as [131]
Citations
Cites
1 retracted
Vimal, V., Singh, T., Qamar, S., Nautiyal, B., Singh, K. U., & Kumar, A. (2020) · Computational Intelligence · doi:10.1111/coin.12371 · Cited by 181
Self-citationRetractedImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
(2016) · Cited by 11,679
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Research works printed but not linked: 4
- Xu, J., Li, C., Zhou, Y., Mou, L., Zheng, H., Wang, S.: Classifying mammographic breast density by residual learning. arXiv preprint arXiv:1809.10241 (2018) · arXiv
- Lévy, D., Jain, A.: Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542 (2016) · arXiv
- Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016) · arXiv
- Collobert, R., Bengio, S.: Svmtorch: support vector machines for large-scale regression problems. J Mach Learn Res 1(2), 143–160 (2001)
Cited by
Sorted by citations
Evaluation of artificial intelligence techniques in disease diagnosis and prediction
Nia, N. G., Kaplanoğlu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 498
Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Rana, M., & Bhushan, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14305-w · Cited by 408
Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024) · Bioengineering · doi:10.3390/bioengineering11101034 · Cited by 393
Opportunities of Artificial Intelligence and Machine Learning in the Food Industry
Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021) · Journal of Food Quality · doi:10.1155/2021/4535567 · Cited by 302
Self-citationDeep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction
Nasser, M., & Yusof, U. K. (2023) · Diagnostics · doi:10.3390/diagnostics13010161 · Cited by 249
Metadata
- Authors
- Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A.
- Affiliations
- Graphic Era Hill University (IN)
- UNSW Sydney (AU)
- Banaras Hindu University (IN)
- Corresponding author
- Kumar, Abhishek
- Venue
- Multimedia Systems
- Published
- 2020-09-25
- Volume
- 27
- Issue
- 4
- Pages
- 599-613
- ISSN
- 0942-4962 (ISSN-L)
- 1432-1882
- OpenAlex
- W3088930327
- Semantic Scholar
- 1756b3ce83ed682f957bd37770db00e586486ee5
- Cited by
- 222
- References
- 109
- Citations by year
- 2026: 21 · 2025: 52 · 2024: 47 · 2023: 57 · 2022: 28 · 2021: 16
- FWCI
- 9.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multimedia Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- deep learning in healthcare, medical image processing, diagnosis, medical image classification, medical image segmentation, deep learning architectures, breast cancer, lung cancer, brain tumors, health care system
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.
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