Analysis of Diabetic Retinopathy and Its Causes Using Fuzzy C-Means Model

Abstract

Diabetes Mellitus, commonly known as Diabetes, is a caused due to high range of sugar in the human body. After a period of time diabetes will lead to a deficiency in eye called as Diabetes Retinopathy. The major symptoms of this disorder are bulging of blood vessels, small lesions and other eye related eyes. The idea of our project is to analyze the severity level of the diabetes retinopathy using three different training methods. Deep learning plays a major role in the project. Proposed Model has been trained with three types, back propagation NN, deep neural networks called DNN and convolutional neural network known as CNN. The Deep Learning models created by using the above neural networks are able to measure the features such as micro aneurysms, blood vessels, fluid drip into different class of categories and hemorrhages. Model will find out the value or level of infection in the patient's eye. Fuzzy C- means algorithm are particularly used to calculate severity of the infection caused and the type of infection also. By this paper, severity of the Retinopathy can be found. The entire system has a user-friendly environment which makes the identification easy. Once the test image is uploaded, the interface will have buttons in order to do the necessary transformation on the given image. At the end of the complete interface options, the level of the infection along with the narrowed down area of infection will be seen in the interface.