Analysis of Large-Scale Diabetic Retinopathy Datasets Using Texture and Blood Vessel Features

Abstract

Diabetic retinopathy is a disease caused by the complications of diabetes mellitus and can cause blindness. In this study, we classified the stages of diabetic retinopathy using a large-scale dataset that consists of 35,126 fundus images. The classification of diabetic retinopathy includes five stages, from normal to proliferative diabetic retinopathy. In the proposed approach, a morphological feature extraction method and advanced local binary patterns were employed to extract blood vessel and texture features, respectively. The support vector machine, K-nearest neighbor, random forest, and logistic regression techniques were compared as classifiers. The classification was conducted on fundus images from a Kaggle dataset. The experimental results show that the texture feature extraction method based on advanced local binary patterns leads to higher accuracy, precision, and recall score than the blood vessel features extracted using morphological operators.