Diabetic retinopathy related lesions detection and classification using machine learning technology

Abstract

A novel Computer Aided Diagnosis System for early diagnosis of Diabetic Retinopathy is proposed for the detection and classification of Bright lesion classes and Dark lesion classes of Fundus Retina images using machine learning mechanisms. In the proposed methodology, the detection procedure is based on Fuzzy C Means (FCM) clustering technique to segment the candidate region areas. In the Dark lesion category, attempts are being made to modify the Micro aneurysms detection and Blood vessel elimination with the help of improvised algorithms. For the classification of each Bright and Dark lesion classes a classification system is built using machine learning algorithms namely Naive Bayes and Support Vector Machine. A comparative study between the two machine learning algorithms yield accuracy of 97.0588% for Bright lesion classification using Naive Bayes classifier and accuracy of 88.8889% for Dark lesion classification using Support Vector Machine classifier.