Detection of Multiple Ocular Diseases Using Machine Learning

Human eye is one of the compound sensory organs which is responsible for providing color vision. Any disturbance in eyes, or aging might lead to serious problems such as Glaucoma, Cataract, Hypertensive Retinopathy, macular degeneration, Pathological Myopia etc.… in the long run. Early and accurate diagnosis of these diseases is essential in effective treatment and prevention of blindness. Machine Learning (ML) has evolved as promising tools for automating the diagnosis of multiple eye diseases. This review work emphasizes on discussing various deep learning algorithms like computational neural network (CNN), K-means algorithm (KNN) and many other algorithms for creating an efficient model to predict the occurrence or non-occurrence of the diseases with high accuracy. The proposed system uses 2 different deep learning algorithms: CNN and KNN for construction of model and prediction. And their efficiencies are compared in accordance with the performance of the models.