Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy

Abstract

Diabetes is a globally prevalent disease that can cause microvascular compilation such as diabetic retinopathy (DR) in the human eye organs, and it might be a significant reason for visual deficiency. The present study aims to develop an automated diabetic retinopathy detection and classification system from digital fundus images to reduce the workload of ophthalmologists. This work comprises three main stages. Our method starts with the extraction of blood vessels from retinal images using Kirsch’s templates. Secondly, the method detects whatever the input images as normal or diabetic retinopathy and then illustrates an automatic diabetic retinopathy classification technique through statistical texture features. It embeds gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) for second-order and higher-order statistical texture features as a feature extraction technique into three-renowned classifiers namely K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). The evaluation results containing a dataset of 644 retinal images indicate that the proposed method based on random forest classifier is found to be effective with a weighted sensitivity, precision, F1-score, and accuracy of 95.53%, 96.45%, 95.38%, and 95.19%, respectively. It is expected that the suggested medical decision support system will be able to detect and classify diabetic retinopathy well.