Automated Diabetic Retinopathy Identification Using Convolutional Neural Network

Classification phase is one of the important step for determining, analysing as well as diagnosing the diabetic retinopathy disorder. Nanostructures include red lesions, retinal hard macular exudates as well as Neovascularization would take up space aroundretina by the reason of devastation of veins. In order to computerise the technique pertaining to diabetic retinopathy phases categorization, a convolution neural network grounded method could be utilized. Colour fundus pictures of retina are collected during this work with aim of diabetic retinopathy classification among 5 phases by utilizing a convolution neural network. Convolution neural network with EfficientNet B5 network is employed for the phase classification of diabetic retinopathy disorder, a Kappa value (classification accuracy) of 88.48% is achieved.