Classification of Different Stages of Diabetic Retinopathy using Convolutional Neural Networks

Abstract

Diabetic Mellitus is the most familiar disease around the globe. Long prevalence of diabetes causes several problems related to health. The most common issue is Diabetic Retinopathy (DR). Diabetic retinopathy is a situation in which the vessels inside the retina are vandalized, leaking harmful substances and fluids in the surrounding tissue resulting in hemorrhages, micro aneurysms in the eye and further into partial or complete vision loss. This disease if treated in the early stage can help to prevent vision loss, but since it takes time for diagnosis and there is a shortage of ophthalmologists' patients suffer vision loss even before diagnosis. Hence, early detection of DR may help in reducing the problem. Therefore, in this paper we investigate various approaches to understand the process of detecting Diabetic Retinopathy as accurately as possible and classifying them into different grades of treatable DR (NPDR) namely LO, L1 DR, L2 DR and Proliferate DR (PDR) using Deep Learning and Image Processing techniques also making some improvisations on the same to enhance the capability of other existing systems.