Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{163086,
        author = {Om Kakade  and Shirsathe Sakshi and Savardekar Suchitra and Ugale Kaustubh},
        title = {Eye Diabetic Retinopathy Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {949-956},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163086},
        abstract = {In this paper, we address the important concerns surrounding diabetic retinopathy (DR), which is the primary cause of blindness in people with the disease. DR, resulting from long-term diabetes, requires early detection to preserve vision. Initial diabetic retinopathy has been identified using artificial intelligence-based technology. Manual diagnosis is time consuming, so machine learning, particularly deep learning, is being used to examine retinal fundus images more efficiently. Deep learning is being highlighted for its role in the early detection and classification of DR. We had implemented a deep learning model that detects different stages of diabetic retinopathy. The review focuses on retinal fundus images, discusses problems and research gaps, and suggests future directions in DR detection.

},
        keywords = {Diabetic Retinopathy, Deep Learning, CNN, VGGnet, fundus images, Microaneurysms, Hemorrhages, Exudates.},
        month = {},
        }