Author:
Haldankar Govind,Galbal Gaurav,Choudhary Vikram,Kanoja Sanket
Abstract
Diabetic retinopathy (DR) is the major cause of vision impairment and blindness in diabetics. Early detection and treatments are critical in preventing irreparable retinal damage. Manual detection of diabetic retinopathy by an ophthalmologist takes a long time, and patients must suffer greatly during this time. This paper presents an automated approach for rapid DR detection using the DenseNet-121 architecture. Our model achieves an accuracy exceeding 80%, with a precision score of 81% and a recall score of 86%, indicating its high effectiveness in detecting DR. Additionally, we developed a server-based implementation where the trained model is deployed. Images captured by a camera are uploaded to a cloud server, which processes them and sends back a diagnostic response. This study contributes to continuing efforts to create efficient and reliable techniques for early DR identification, resulting in earliermanagement and better patient outcomes.
Publisher
International Journal of Innovative Science and Research Technology
Reference41 articles.
1. K. NEM, M. Loey, M. H. N. Taha, and H. N. E. T. Mohamed, ”Deep transfer learning models for medical diabetic retinopathy de- tection,” Acta Inform. Med., vol. 27, no. 5, pp. 327-332, 2019, doi: 10.5455/aim.2019.27.327-332.
2. A. Bajwa, N. Nosheen, K. I. Talpur, and S. Akram, ”A prospective study on diabetic retinopathy detection based on modify convolutional neural network using fundus images at Sindh Institute of Ophthalmology and Visual Sciences,” Diagnostics, vol. 13, no. 3, p. 393, 2023, doi: 10.3390/diagnostics 13030393.
3. R. Revathy, B. S. Nithya, J. J. Reshma, S. S. Ragendhu, and M. D. Sumithra, ”Diabetic retinopathy detection using machine learning,” Int. J. Eng. Res. Technol., vol. 9, 2020.
4. A. Dhakal, L. P. Bastola, and S. Shakya, ”Detection and classification of diabetic retinopathy using adaptive boosting and artificial neural network,” Int. J. Adv. Res. Publ., vol. 3, no. 8, pp. 191-196, 2019. [Online]. Available: http://www.ijarp.org/online-papers-publishing/aug2019.html
5. F. Alzami, A. Arya Megantara, and A. Z. Fanani, ”Diabetic retinopathy grade classification based on fractal analysis and random forest,” in Int. Seminar on Appl. for Tech. of Info. and Comm., 2019.
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