DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING

Diabetic Retinopathy (DR) is an ailment that arises in patients suffering from Diabetes for more than 2 decades. This causes blindness due to elevated blood glucose levels. The condition can be cured when it is diagnosed at an early stage. The currently available methods to diagnose Diabetic Retinopathy is Fluorescein Angiography, which is slow and is not available to masses. In this paper we propose a novel solution by incorporating Convolutional Neural Networks (CNN) to detect the presence of Diabetic Retinopathy using the color fundus image of the patient. We have built a Neural Network architecture consisting of two stages of CNN where the first stage detects the presence of DR and the second stage classifies it into four stages. With this architecture, we have achieved an accuracy of 90.32%. The dataset we used to train the Neural Network was taken from a Kaggle competition that consisted of 3,662 color fundus image