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Diabetic Retinopathy (DR) is a complication of long-standing, uncheckeddiabetes and one of the leading causes of blindness in the world. This paperfocuses on improved and robust methods to extract some of the features of DR,viz. Blood Vessels and Exudates. Blood vessels are segmented using multiplemorphological and thresholding operations. For the segmentation of exudates,k-means clustering and contour detection on the original images are used.Extensive noise reduction is performed to remove false positives from thevessel segmentation algorithm's results. The localization of Optic Disc usingk-means clustering and template matching is also performed. Lastly, this paperpresents a Deep Convolutional Neural Network (DCNN) model with 14 ConvolutionalLayers and 2 Fully Connected Layers, for the automatic, binary diagnosis of DR.The vessel segmentation, optic disc localization and DCNN achieve accuracies of95.93%, 98.77% and 75.73% respectively. The source code and pre-trained modelare available https://github.com/Sohambasu07/DR_2021

Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images