Diabetic Retinopathy affects the eyes, potentially causing partial or total damage to the retina. High blood glucose levels harm the nerves carrying blood in retina, leading to microvascular end-organ damage that is associated with diabetes. Efficient implementation of preventive measures and minimizing potential harm heavily relies on early detection. Researchers have explored computational approaches to detect this disease. Cutting-edge studies employ image processing techniques and deep learning models to facilitate the timely identification of diabetic deposits, leading to decreased retinal damage. This article recommends using CNN (such as RESNET 50, VGG, and Xception) on fundus images to identify diabetic deposits in the dataset.