Diabetic Retinopathy Detection by Retinal segmentation with Region merging using CNN

Diabetic retinopathy is a state of eye infirmity in which damage arises due to diabetes mellitus. It is one of the prominent reasons behind blindness. Most of new cases of diabetic retinopathy can get reduced with proper treatment of eyes. The method proposed in this paper aims at detecting the various stages of Diabetic Retinopathy by using U-Net segmentation with region merging & Convolutional Neural Network(CNN) to automatically diagnose and thereby classify high-resolution retinal fundus images into 5 stages of the disease based on severity. A major difficulty of fundus image classification is high variability especially in the case of Proliferative diabetic retinopathy where there exist retinal proliferation of new blood vessels and retinal detachment. Hence, The proper analysis of retinal vessel is required to get the precise result, which can be done by Retinal Segmentation. Retinal Segmentation is the process of automatic detection of boundaries of blood vessels. The features lost during segmentation are retained during region merging & passed through the image classifier, with the accuracy up to 93.33%.