Glaucoma Diseased Image Classification Using Different Classifiers

The recent advance in glaucoma classication method and improvements in the ac- curacy of classication. Glaucoma is the wrost eye disease it results into permanent blindness so to avoid blindness early detection of glaucoma is essential. Our research is focused automated classication system for the identication of disease, it is used to extract textural features from retinal images which are use to distinguish between normal and infected diseased samples. The eectiveness is gauged of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and Nave Bayes classication techniques. This represents detailed review on existing classication approaches that have applied to glaucoma classication. We observed an accuracy of around 94 % using SVM classifier.