The changes in lifestyles of people and the lack of focus and care on the health issues have led to a rise in the number of diabetic patients. Diabetic Retinopathy is a complication of diabetes which is determined by the damaging of blood vessels in eye. It is the leading cause of vision impairment that can lead to blindness. For detection and grading, retinal image has to be operated on to see the symptoms of the disease and its complexity. With the advancement in technologies, availability of high-end Computing System, availability of large amount of data and new methodologies to process the data, we can overcome the problem we faced before and create awareness among the individuals. In the proposed work, we have built a two-stage Detection and grading system using Deep Neural Network model and compared the efficiencies of Convolutional Neural Network. For our analysis, we have made use of the publicly available dataset. The pictures are divided into 5 classes as per severity levels and the number of images in the classes was unbalanced. Having a large number of such images and we have decided to use Deep neural networks for classification because of the limitation of conventional machine learning techniques to operate with very huge amount of Pictorial data. After pre-processing the dataset, we have performed hyper parametric optimisation in the stock neural networks architecture for increasing the prediction accuracy which has come to be 98 % accuracy. After testing on the stock network architectures with different activation functions, number of epochs, kernel sizes, we have also introduced an optimised custom neural network for DR screening-grading system which has come to an accuracy of 91%.