Prediction of Retinal diseases using Convolutional Neural Networks

Abstract

Abstract: Eye diseases are the common diseases people are facing nowadays. Even people of all ages ranging from children to old are affected due to eye diseases. Some diseases will become very complex if they are untreated at an early stage. To overcome such problems early detection and treatment is required which could prevent vision loss. For many, eye disorders have become almost a part of their daily lives. Traditionally, retinal disease detection techniques were based on manual description. The WHO (World Health Organization) finds that there are millions of visually impaired people all over the world. The retinal diseases include Cataract, Crossed Eyes, Bulging Eyes, Uveitis and Glaucoma can be predicted using convolutional neural networks in this project. Medical Imaging has a huge scope for the application of recent advancements in computation. With the emerging computer technology, the development of an automatic system for diseases using Convolutional neural Networks in association with different optimization methods (SGDM, RMS-PROP, and ADAM). Treating the disease is now possible especially if the patient’s area and medical services are limited. Thus, the idea is to build an algorithm that automatically identifies whether a patient is suffering from any eye diseases or not by looking at the images