Diabetic Retinopathy detection using Weighted Filters and Classification using CNN

Diabetic retinopathy is one of the primary sources of blindness in the developed society of over 50 percent. The early diabetic diagnosis of retinopathy may help prevent severe risks of vision loss. A great deal of the analysis already done focuses on the manual retrieval of properties. Still, this essay attempts to diagnose diabetes retinopathy automatically in their multiple groups to include a new approach focused on a deep-neuronal network of Gray Wolf optimization (GWO). The Gaussian space theory and generic data structures improve the accuracy and quantity of the dataset to overcome unbalanced dataset problems. Original and added data sets are used to train and validate the proposed model. The model precision of the convolutional neural network in this paper for an increased data collection is 0.9691, which is ideal compared to the original data and other procedures.