The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Deep neural networks to predict diabetic retinopathy
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Citations
Cites
1 retracted
Hemanth, D. J., Deperlioglu, O., & Kose, U. (2020) · Neural Computing and Applications · doi:10.1007/s00521-018-03974-0 · Cited by 285
RetractedXGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 19,343
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
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
Global prevalence and major risk factors of diabetic retinopathy
Yau, J. W. Y., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., Chen, S.-J., Dekker, J. M., Fletcher, A., Grauslund, J., et al. (2012) · Diabetes Care · doi:10.2337/dc11-1909 · Cited by 4,985
Deep Learning Approach for Intelligent Intrusion Detection System
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019) · IEEE Access · doi:10.1109/access.2019.2895334 · Cited by 1,946
Automated Identification of Diabetic Retinopathy Using Deep Learning
Gargeya, R., & Leng, T. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.02.008 · Cited by 1,362
The Prevalence of Diabetic Retinopathy Among Adults in the United States
(2004) · Archives of Ophthalmology · doi:10.1001/archopht.122.4.552 · Cited by 1,148
Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.16-19964 · Cited by 1,101
Cited by
R.M., S. P., Maddikunta, P. K. R., M., P., Koppu, S., Gadekallu, T. R., Chowdhary, C. L., & Alazab, M. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.05.048 · Cited by 470
Self-citationEarly-Stage Alzheimer’s Disease Prediction Using Machine Learning Models
Kavitha, C., Mani, V., Srividhya, S. R., Khalaf, O. I., & Tavera Romero, C. A. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.853294 · Cited by 373
Blockchain for Edge of Things: Applications, Opportunities, and Challenges
Gadekallu, T. R., Pham, Q.-V., Nguyen, D. C., Maddikunta, P. K. R., Deepa, N., Prabadevi, B., Pathirana, P. N., Zhao, J., & Hwang, W.-J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3119639 · Cited by 217
Self-citation
Metadata
- Authors
- Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G.
- Venue
- Journal of Ambient Intelligence and Humanized Computing
- Published
- 2020-04-24
- Volume
- 14
- Issue
- 5
- Pages
- 5407-5420
- ISSN
- 1868-5137 (ISSN-L)
- 1868-5145
- OpenAlex
- W3018026610
- Cited by
- 283
- References
- 38
- FWCI
- 21.29
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep neural networks to predict diabetic retinopathy. https://app.cochat.ai/papers/10.1007/s12652-020-01963-7
- The paper
- Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G. (2023). Deep neural networks to predict diabetic retinopathy. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01963-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
