Early Detection and Classification of Diabetic Retinopathy Using Empirical Transform and SVM

Abstract

Diabetic Retinopathy is the name given to ‘disease of retina’. The objective of this work is for timely diagnosis and classification of diabetic retinopathy using curvelet transforms and SVM. Firstly, retinal images are enhanced using empirical transform. Canny edge detection is applied for extracting eyeball from retinal fundus image. Then morphological operations are applied for locating the imperfections in the images. At the end, images are classified into normal, proliferative or non-proliferative by using SVM. Both accuracy and sensitivity of the images is improved when compared with previous technique in which only k-means and fuzzy classifier is used. The number of exudates detected in present work is more than that of the process without enhancement. The sensitivity, specificity and accuracy of system are calculated as 96.77, 100 and 97.78 respectively.