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@article{206794,
        author = {K Skanda Shetty and Prof. Clitus Neil D Souza and P S Chirag and Satvik Chandrakant Moger and Vilas Rathnakar Naik},
        title = {Diabetic Retinopathy Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {464-469},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206794},
        abstract = {Diabetic Retinopathy (DR) is one of the leading causes of blindness among diabetic patients worldwide. Early detection is essential to prevent severe vision impairment, but traditional diagnostic methods are time-consuming and require expert ophthalmologists. This paper presents an automated system for detecting diabetic retinopathy using deep learning techniques. The proposed system utilizes Convolutional Neural Networks (CNN) and transfer learning models such as EfficientNetB2 to classify retinal fundus images into different severity levels. Image preprocessing techniques such as resizing, normalization, and contrast enhancement are applied to improve model performance. The trained model is deployed using a web-based interface for real-time prediction. The system demonstrates high accuracy and efficiency, making it a valuable tool for large-scale screening and assisting medical professionals in early diagnosis},
        keywords = {Diabetic Retinopathy, Deep Learning, CNN, EfficientNetB2, Image Processing, Medical Imaging, Artificial Intelligence},
        month = {July},
        }