:Diabetic retinopathy is a leading problem throughout the world and many people are losing their vision because of this disease. The damage in the retinal blood vessel eventually blocks the light that passes through the optical nerves which makes the patient with Diabetic Retinopathy blind. Therefore, in our research we wanted to find out a way to overcome this problem and thus using the help of Convolutional Neural Network, we were able to detect multiple stages of severity for Diabetic Retinopathy. Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the eye retina. Early detection of Diabetic Retinopathy is crucial in order to sustain the patient’s vision effectively. The main issue involved with DR detection is that the manual diagnosis process is very time, money, and effort consuming and involves an ophthalmologist’s examination of eye retinal fundus images. The latter also proves to be more difficult, particularly in the early stages of the disease when disease features are less prominent in the images. In our research we wanted to find out a way to overcome this problem and thus using the help of Convolutional Neural Network, we were able to detect multiple stages of severity for Diabetic Retinopathy. one such process is manual screening. With photos of eyes as input, the goal of this project is to create a new model, ideally resulting in realistic clinical potential.