Classification of visualization exudates fundus images results using support vector machine

Abstract

This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes.images (81 normal and 68 exudates) from MESSIDOR databas the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using SVM2 to determine its accuracy. pixels in fundus image using linear kernel function of SVM1 to diagnose DR. an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM);