Diabetic Retinopathy (DR) is a major cause of blindness. Exudates are one of the primary signs ofdiabetic retinopathy which is a main cause of blindness that could be prevented with an early screeningprocess In this approach, the process and knowledge of digital image processing to diagnose exudatesfrom images of retina is applied. An automated method to detect and localize the presence of exudatesand Maculopathy from low-contrast digital images of Retinopathy patient’s with non-dilated pupils isproposed. First, the image is segmented using colour K-means Clustering algorithm. The segmentedimage along with Optic Disc (OD) is chosen. To Classify these segmented region, features based oncolour and texture are extracted. The selected feature vector are then classified into exudates and nonexudatesusing a Support Vector Machine (SVM) Classifier. Also the detection of Diabetic Maculopathy,which is the severe stage of Diabetic Retinopathy is performed using Morphological Operation. Using aclinical reference standard, images with exudates were detected with 96% success rate. This methodappears promising as it can detect the very small areas of exudates.