An Ensemble Classifier Based on Individual Features for Detecting Microaneurysms in Diabetic Retinopathy

Abstract

Individuals with diabetes are more likely to develop Diabetic Retinopathy (DR), a chronic ailment that can lead to blindness if left undiagnosed. Microaneurysms (MA) are tiny red lesions that form on the retina in the early stages of Diabetic Retinopathy (DR). This paper investigates a unique approach for the automated early identification of microaneurysms in eye images. A unique ensemble classifier technique is suggested in this work. Classifiers like SVM, KNN, Decision Tree, and Naïve Bayes are chosen in this study for building an ensemble model. After preprocessing the image, certain common image characteristics such as shape and intensity features were retrieved from the candidate. The mean absolute difference of each feature is computed. Based on mean ranges that would give improved classification results, an expert classifier is chosen and trained. The outputs of the classifiers are integrated for each of the distinct characteristics, and the number of categories that have been most frequently repeated is utilized to reach a final decision. The process has been comprehensively validated using two available open datasets, like e-ophtha and DIARETDB1. On the e-ophtha and DIARETDB1, the ensemble model achieved an AUC of 0.928 and 0.873, sensitivity of 90.7% and 85%, specificity of 90% and 91% respectively.