Deep Learning Radial Basis Function Neural Networks Based Automatic Detection of Diabetic Retinopathy

Abstract

In this project, we propose a new novel DNN-based automatic detection of diabetic retinopathy. In deep neural networks are used for classify the images that indicate diabetic retinopathy. The main aim of this project is to find the suitable way to detect the problems and classify them. We propose an deep neural network (RBFNN) classifier gives high precision in grouping of these disease through spatial examination. The RBFNN classifier does not require an large training time, therefore the model production can be expedited. We further find from our data set of 80,000 images used in our proposed RBFNN achieves a sensitivity of 95% and an accuracy of 75% on 5000 validation images. The fuzzy c means clustering is used to store the information as the processed images in this project. Finally, the proposed system is developed using matlab simulation.