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@article{189832,
        author = {Ranjan P and Sasikala P and Kanishkanth M S and Manoj M and Muhammad Yunus S},
        title = {RetinaVision -Web application for the detection of Retinal Diseases using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {348-352},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189832},
        abstract = {This paper presents the project titled “RetinaVision”, an ML-based web application developed to assist in the early detection of retinal diseases through automated image analysis. Retinal diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen are among the leading causes of vision impairment and blindness worldwide. Early diagnosis plays a critical role in preventing irreversible vision loss; however, traditional screening methods require specialized equipment and expert ophthalmologists, which may not be readily available in all regions. Built using a modern web stack with a React-based frontend and a Flask-powered backend for AI inference, the application ensures usability, speed, and accessibility. RetinaVision addresses this challenge by leveraging deep learning techniques to analyze retinal fundus images uploaded by users. RetinaVision is designed as an assistive diagnostic tool to support healthcare professionals and promote early awareness, rather than replacing clinical judgment.},
        keywords = {},
        month = {January},
        }