Authors - B.Usha Rani, M.Sudhakar, A.Srivani, Y.Surya Praveen Abstract - The purpose of Diabetic Retinopathy Prediction is to use computer technology to identify early stages of retinal damage caused by diabetes. Since diabetic retinopathy can lead to blindness or permanent vision impairment if not treated in a timely manner, accurate and rapid diagnosis is vital. Recent tech niques for diagnosing diabetic retinopathy require an ophthalmologist to perform a manual examination of the eye’s retina with the use of fundus photography. The diagnostic process can be costly, time-consuming, and vary significantly from one person to another. A large percentage of diabetes patients live in rural areas, where it is difficult or impossible for them to have periodic screening by a diabetic specialist or receive healthcare services. There is a need to develop a solution to these problems, and the Diabetic Retinopathy Prediction System uses deep learning based techniques to analyze retinal fundus images and produce pre dictions regarding diabetic retinopathy. Analysis of the retinal fundus images will include preprocessing, feature extraction using CNNs, and automated classifica tion into diabetic retinopathy by degree and severity. This approach increases the accuracy and consistency of diabetic retinopathy diagnosis while minimizing the need for human input. The proposed system will allow for early identification of diabetic retinopathy in resource poor environments, support large scale screening programs and aid in clinical decision making by ophthalmologists. Additionally, the system has potential integration into mobile health systems and tele-ophthal mology networks. Experimental results indicate the proposed system is capable of accurately detecting diabetic retinopathy with high levels of specificity and sensitivity.
Open Zoom