Diabetic retinopathy classification using deep convolutional neural network

<span>Diabetic retinopathy (DR) is a diabetic impairment that affects the eyes and if not treated could lead to permanent vision impairment. Traditionally, Ophthalmologists perform diagnosis of DR by checking for existence any seriousness some subtle features in fundus images. This process very efficient as it takes lot time resources. testing all patients, which are undiagnosed or untreated, big task due inefficiency traditional method. paper was written with aim propose classification system based on an deep convolution neural network (DCNN) model computationally efficient. Amongst other supervised algorithms involved, proposed solution find way efficiently classify images into 5 different levels severity. Application segmentation after pre-processing then use convolutional networks dataset results high accuracy 91.52%. The result achieved given limitations computational powers.</span>