Improvised Detection of Diabetic Retinopathy Using Fast R CNN

Abstract

Diabetic Retinopathy (DR) is a medical disease that affects the retina in the eyes of people who have diabetes mellitus. It has become a major cause of blindness in diabetes patients as it affects nearly eighty percent of patients afflicted with this disease for longer periods of time. Through this paper, we propose a method for improved detection of diabetic retinopathy by extracting its distinctive features from fundus images and classifying them as DR affected or normal.  Firstly, preprocessing of images is done by performing morphological transformations.  Then, extraction of features and classification is done using a Fast R-CNN (Fast-Region based convolutional neural network). This paper demonstrates that the efficiency and accuracy of our proposed model using Fast R-CNN is better than other conventional approaches. Our model was able to achieve an AUC score of 0.9298.