SIFT FEATURE BASED DETECTION OF GLAUCOMA
The normal and glaucomatous fundus images are classified using Support Vector Machines (SVM) and Naive Bayes in this paper. The features of both classes of images are extracted using the Scale Invariant Feature Transform (SIFT) Two optimal features are then utilized by the SVM Classifier to classify the images into the two respective categories and its performance is compared with that of the Naive Bayes classifier. Keywords Glaucoma, Naive Bayes, Scale Invariant Feature Transform, Support Vector Machine.