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{177275,
        author = {Mallepati Naga Mano Shiva Lasya and Deekshitha Chalamalla and Sai Sowmya Ramayanam and Kanchanapally Bharath Teja Goud and K.M.N.Vardhini and Dr.M.Ramesh},
        title = {Diabetic Retinopathy Screening: A Comprehensive Machine Learning Framework For Early Detection And Diagnosis Using Retinal Imaging},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {409-414},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177275},
        abstract = {Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients, and early detection is crucial for effective management. This screening system leverages advanced image analysis combined with patient input data such as medical history, blood sugar levels, and duration of diabetes to stratify patients based on their risk. Alongside classifying the severity of diabetic retinopathy from retinal images, the system provides a real-time, personalized risk assessment. For patients identified as high-risk, it recommends tailored management plans, including more frequent screenings, targeted lifestyle modifications like diet changes, and timely alerts for medical interventions or specialist referrals. For patients unable to upload retinal images, the system provides a dynamic questionnaire highlighting common symptoms of diabetic retinopathy. Using an intuitive checkbox interface, it evaluates symptom selections to assess the likelihood of DR. If a potential risk is detected, users are immediately advised to consult ahealthcare professional. The system also delivers personalized educational resources based on each patient's risk profile, helping them understand their condition, follow treatment recommendations, and make informed health decisions.},
        keywords = {Convolution Neural Networks (CNN), Early Detection,Machine Learning,Retinal Image Analysis.},
        month = {April},
        }