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
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“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
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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
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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
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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
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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
Items 201–222 of 222
AI and emerging technologies for diagnosis
Soundararajan, S. S., Selvakumar, J., Vijayalakshmi, S., & Baskaran, N. (2025) · Progress in brain research · doi:10.1016/bs.pbr.2025.08.004
A Deep Learning Approach for Predicting Ejection Fraction from Pediatric Echocardiogram
Riskhan, B., Ahmed, R., Kemal, R. S., Djelloul, Y., Hasanat, M. H. A., & Narputro, P. (2025) · International Conference on Metaverse and Current Trends in Computing (ICMCTC) · doi:10.1109/icmctc62214.2025.11196617
Explainable Artificial Intelligence for Trustworthy Decision-Making in Smart Wearable Textiles
Kumar, P., Singh, M., Yadav, J., Chaudhary, S., & Saxena, P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4287-0.ch009
22911 Artificial intelligence in diagnostics
Latha, A. H. D. P., Padmavathi, S., Varalakshmi, B., & Padma, D. A. (2025) · De Gruyter eBooks · doi:10.1515/9783111548777-011
Exploring Quantum Machine Learning for PCOS Diagnosis: A Comparative Analysis
Sharma, K., & Jain, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06250-5_21
Alebel, H., Meshesha, M., Jifara, W., & Asefa, G. (2026) · Research Square · doi:10.21203/rs.3.rs-8447504/v1
Fizyoterapide Teknoloji: Fizyoterapi ve Rehabilitasyonda Yapay Zekâya Dair Bibliyometrik Bir Analiz
Aytutuldu, G. K., Aytutuldu, İ., Birinci, T., & Akgül, Y. S. (2025) · DergiPark (Istanbul University)
Artificial intelligence-enhanced diagnostics: deep learning in medical imaging
Kaur, H., & Singh, G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33082-7.00011-5
Spatial Opinion-Driven Community Detection in Social Networks
Boutsikaris, L., & Polykalas, S. E. (2025) · IEEE International Conference on Artificial Intelligence, Computer Science and Information Processing (AICSIP) · doi:10.1109/aicsip65423.2025.11427219
Nirmala Devi, M., Krishna, B., Chengareddy, P., & Naveen, K. (2025) · International Conference on Emerging Computation and Information Technologies (ICECIT) · doi:10.1109/icecit67774.2025.11451074
Taneja, K., Walia, D., Tyagi, A., Singh, H., & Mourya, A. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44111-0.00023-8
Research on Coal Gangue Detection and Identification Method Based on Improved YOLOv7
Hou, D., Yang, Y., Liu, Y., Bian, G., Li, Z., Du, M., Zhao, L., Zhang, P., & Zhang, X. (2026) · Processes · doi:10.3390/pr14081227
A secure IoT-based smart resource management framework for sustainable e-governance systems
Ruchika, Ansari, M., & Wadhwani, G. K. (2026) · EDPACS · doi:10.1080/07366981.2026.2663536
A Data-Driven Legal Analytics Framework for Healthcare Litigation Outcome Prediction
Sebastian, T., Philip, A. C., Panwar, P., Jwala, V. C., Kalaimagal.V, & Pandiaraj, S. (2026) · IEEE International Conference on AI Engineering and Innovations (AIEI) · doi:10.1109/aiei69164.2026.11497538
Monazah, S. M. M., Salehi, M. R., Fallahi, V., Kordrostami, Z., & Hosseini, M. (2026) · Journal of Optics · doi:10.1007/s12596-026-03199-8
Kumar, L., Parwaliya, A., Kumar, M., Ansari, M. A., & Mewada, A. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.163
Symmetry-Driven Enhanced Auxiliary Classifier GAN for Data-Efficient Breast Tumor Classification
Marasović, T., & Papić, V. (2026) · Symmetry · doi:10.3390/sym18071235
Mishra, P., & Das, H. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-19215-8_11
AI-Enabled Wastewater and Environmental Sensor Networks for Early Bio Surveillance
Mazhar, F., Fatima, E., Sajid, A. A., Hamdani, E., Naqvi, H. A., Amjad, R., Sulman, A., Sultana, S., Afzal, A., & Mansoor, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9033-8.ch011
Jaswal, R., Khullar, V., & Panda, S. N. (2026) · SN Computer Science · doi:10.1007/s42979-026-05257-1
Sayed, M., & Mohamed, H. (2026) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2026.143018
The benefits and biases of artificial intelligence in microbiology
Aransiola, S. A., Onyinoyi, A. H., Akhigbe, J. O., Onoruoiza, S. I., Esenogho, E., & Maddela, N. R. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109457
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.
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