Diabetes can cause extensive destruction in both the acquiring and modernized societies. The fast growing effects of it causes serious complications like morbidity and later to diabetic retinopathy which results to blindness. Gibing the feature of the disease at earliest stage takes an interval of time by treatment with Laser. Initial stage of Diabetes is presented by NPDR, shows their significance by the earliest vessels change in the retina. NPDR has three classes i.e. mild, moderate and severe. Initial changes when the microaneurysms (MA) start appearing followed by haemorrhages, that leads to cotton wool spots & exudates that finally leads to sever NPDR. PDR is occurred due to neo vascularization (NV) [2]. In our work we are identifying the feature of exudates from the image. On basis of their pixels intensity and frequency it classified into moderate stage of NPDR. Accuracy to the extracting feature is then tested with the perception of the ophthalmologist’s. Firstly raw dataset (Fundus Retinal Image) is pre-processed by morphological technique as images are of variant size, colour contrast and resolution. Then adaptive threshold and centroid is calculated by Otsu methodology so the Image boundary is traced. Then optic disk is localised by calculating ROI using Hough Transformation and distant from the image as the intensity of exudate and the optic disk is same in the fundus image. The SVM classifier uses features extracted by combined 2DPCA instead of explicit image features as the input vector Combined 2DPCA is proposed and then for acquiring higher accuracy of classification we can use virtual SVM. Proposed work focusing on a Sensitivity of 97.1% for the classifier and the Specificity is of 98.3%. Thus a segmented based designed tool detects the effective cause of exudate that leads to moderate NPDR at its earliest stage of mild NPDR. By this tool Specialist gets support in screening a detection of early changes causing Diabetic Retinopathy and hence timely intervention leading to reduced DR related blindness.