Detection and Classification of Retinal Fundus in Diabetic Retinopathy using Modern Artificial Intelligence and Machine Learning Approaches

Diabetic Retinopathy (DR) is a leading cause of vision loss worldwide. Early detection and timely intervention are crucial for preventing vision impairment. This research aims to develop an AI-powered system for the early detection of Diabetic Retinopathy using fundus images. The proposed system utilizes advanced machine learning techniques to analyze fundus images and identify early signs of Diabetic Retinopathy. By leveraging annotated datasets, the model is trained to accurately classify images as healthy or indicative of Diabetic Retinopathy. The developed system offers a potential solution to the challenges associated with traditional DR screening methods, such as the timeconsuming nature of manual examination and the need for specialized equipment. By enabling early detection, this technology can significantly improve patient outcomes and reduce the burden of vision loss due to Diabetic Retinopathy.