ARCHIVES
Original Article
AI Model for Diabetic Retinopathy and Hypertensive Retinopathy Classification on Fundus Images
M Supraja1
Vasudev Bhandari2
Yashawanth Kumar K3
Tejas Kumar S M4
1 2 3 4 Department of Computer Science and Engineering Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Published Online: November-December 2025
Pages: 130-137
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250506033
References
1. Gulshan, V., et al. (2016), “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs”. JAMA, 316(22), 2402– 2410. https://doi.org/10.1001/jama.2016.17216.
2. Abràmoff, M. D., et al. (2016), “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Ophthalmology & Visual Science”, 57(13), 5200–5206. https://doi.org/10.1167/iovs.16-19964.
3. Ting, D. S. W., et al. (2017), “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations”. JAMA, 318(22), 2211–2223. https://doi.org/10.1001/jama.2017.18152.
4. Pratt, H., Coenen, F., Broadbent, D., Harding, S., & Zheng, Y. (2016), “Convolutional Neural Networks for Diabetic Retinopathy”. Procedia Computer Science, 90, 200–205. https://doi.org/10.1016/j.procs.2016.07.014.
5. Wong, T. Y., & Mitchell, P. (2004), “ Hypertensive Retinopathy”. New England Journal of Medicine, 351(22),2310–2317.https://doi.org/10.1056/NEJMra032865.
6. Olsen, T. W., et al. (2019), “Automated Hypertensive Retinopathy Classification UsingFundus Images”. IEEE EMBC. https://doi.org/10.1109/EMBC.2019.
7. Zhang, Z., Wang, Y., & Li, H. (2020), “Deep Learning-Based Detection of Hypertensive Retinopathy from Fundus Images”. Computers in Biology and Medicine, 121, 103594. https://doi.org/10.1016/j.compbiomed.2020.103594
8. Decencière, E., et al. (2012), “Feedback on a Public Database for the Evaluation of Retinal Image Analysis Algorithms”. Medical Image Analysis, 16(1),87–102. https://doi.org/10.1016/j.media.2011.05.002.
9. Li, S., et al. (2020), “Diagnostic Assessment for Diabetic Retinopathy via Deep Learning Using Fundus Images”. Medical Image Analysis, 64, 101742. https://doi.org/10.1016/j.media.2020.101742.
10. He J. (2016),“ Deep Learning for Image Recognition” CVPR.https://doi.org/10.1109/CVPR.2016.90
11. Bellemo, V., et al. (2019), “Artificial Intelligence Using Deep Learning to Screen for Referable and Vision-Threatening Diabetic Retinopathy”. JAMA
2. Abràmoff, M. D., et al. (2016), “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Ophthalmology & Visual Science”, 57(13), 5200–5206. https://doi.org/10.1167/iovs.16-19964.
3. Ting, D. S. W., et al. (2017), “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations”. JAMA, 318(22), 2211–2223. https://doi.org/10.1001/jama.2017.18152.
4. Pratt, H., Coenen, F., Broadbent, D., Harding, S., & Zheng, Y. (2016), “Convolutional Neural Networks for Diabetic Retinopathy”. Procedia Computer Science, 90, 200–205. https://doi.org/10.1016/j.procs.2016.07.014.
5. Wong, T. Y., & Mitchell, P. (2004), “ Hypertensive Retinopathy”. New England Journal of Medicine, 351(22),2310–2317.https://doi.org/10.1056/NEJMra032865.
6. Olsen, T. W., et al. (2019), “Automated Hypertensive Retinopathy Classification UsingFundus Images”. IEEE EMBC. https://doi.org/10.1109/EMBC.2019.
7. Zhang, Z., Wang, Y., & Li, H. (2020), “Deep Learning-Based Detection of Hypertensive Retinopathy from Fundus Images”. Computers in Biology and Medicine, 121, 103594. https://doi.org/10.1016/j.compbiomed.2020.103594
8. Decencière, E., et al. (2012), “Feedback on a Public Database for the Evaluation of Retinal Image Analysis Algorithms”. Medical Image Analysis, 16(1),87–102. https://doi.org/10.1016/j.media.2011.05.002.
9. Li, S., et al. (2020), “Diagnostic Assessment for Diabetic Retinopathy via Deep Learning Using Fundus Images”. Medical Image Analysis, 64, 101742. https://doi.org/10.1016/j.media.2020.101742.
10. He J. (2016),“ Deep Learning for Image Recognition” CVPR.https://doi.org/10.1109/CVPR.2016.90
11. Bellemo, V., et al. (2019), “Artificial Intelligence Using Deep Learning to Screen for Referable and Vision-Threatening Diabetic Retinopathy”. JAMA
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