Diabetic Retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness. An automated system for the early detection of DR can save a patient vision and can also help the ophthalmologist in screening of DR which contains different types of lesion, i.e., micro aneurysms, hemorrhages, exudates. Early diagnosis by regular screening and treatment is beneficial in preventing visual impairment and blindness. This project presents a method for detection and classification of exudates in colored retinal images. Patients with diabetes often develop ophthalmic complications, such as corneal abnormalities, glaucoma, iris neovascularization, cataracts, and neuropathies. The most common and potentially most blinding of these complications, however, is diabetic retinopathy, which is, in fact, the leading cause of new blindness in persons aged 25-74 years in the United States. Approximately 700,000 persons have proliferative diabetic retinopathy, with an annual incidence of 65,000. An estimate of the prevalence of diabetic retinopathy in the United States showed a high prevalence of 28.5% among those with diabetes aged 40 years or older It eliminates the replication exudates region by removing the optic disc region. Several image processing techniques including Image Enhancement, Segmentation, Classification, and registration has been developed for the early detection of DR on the basis of features such as blood vessels, exudes, hemorrhages and micro aneurysms. This project presents a review of latest work on the use of image processing techniques for DR feature detection. Image Processing techniques are evaluated on the basis of their results. Exudates are found using their high gray level variation, and the classification of exudates is done with exudates features and SVM classifier.