An Efficient Automated System for Detection of Diabetic Retinopathy from Fundus Images Using Support Vector Machine and Bayesian Classifiers

Abstract—An Efficient Automated System for Detection of Diabetic Retinopathy from Fundus Images Using Support Vector Machine and Bayesian Classifiers. The preliminary signs of diabetic retinopathy include micro aneurysms, haemorrhages and exudates. Early < Final Year Projects > diagnosis and timely treatment can prevent vision loss in patients with long term diabetes. In this paper we used two algorithm based on filtering operations, morphological transformation and region growing method to extract features for detection of micro aneurysms, haemorrhage and non linear diffusion segmentation followed by colour histogram based clustering techniques is used to differentiate hard and soft exudates. Experimental evaluation of the algorithm has been done with images collected from Deepam Eye Hospital, Chennai, Tamilnadu, India and a database consisting of 77 abnormal and 20 normal images was created. In addition performance of the proposed algorithm is also verified on the publically available DIARETDB0 database. Based on the features obtained, each image is classified as normal or abnormal with Support Vector Machine, Bayesian Network. Classification rate of 95% is obtained with SVM and 90% with Bayesian classifier.