International Journal of Science, Engineering and TechnologyAuthors: Prof. A.C.Sawant, Dangi Ritisha, Jaykar Gayatri, Dudhate Kavita, Samuel Kumarkunta
Abstract: Diabetic Retinopathy (DR) is a serious eye disease caused by prolonged diabetes and is one of the leading causes of blindness worldwide. Early detection is crucial to prevent vision loss, but manual screening is time-consuming and requires expert ophthalmologists. This paper presents a deep learning-based approach for automatic detection and classification of diabetic retinopathy using retinal fundus images. Convolutional Neural Networks (CNN) are employed to extract features and classify images into different stages such as No DR, Mild, Moderate, Severe, and Proliferative DR. The proposed model is trained on publicly available datasets and optimized using preprocessing techniques such as image normalization, augmentation, and noise reduction. Experimental results show high accuracy and reliability, demonstrating the effectiveness of deep learning in medical image analysis. This system can assist healthcare professionals in early diagnosis and improve patient outcomes.
