Eye Disease Detection using CNN

Abstract

Medical professionals such as ophthalmologists frequently use fundus images, which are particularly useful in detecting different retinal problems. They used this to diagnose many eye conditions, including pathological myopia, glaucoma, cataracts, hypertension, and age-related macular degeneration. These fundus pictures can also be utilized to anticipate how severe a disease would be and to identify early warning indicators. In the field of medical science, machine learning algorithms have become increasingly important in recent times. This is also the case in the field of ophthalmology. Our goal in this work is to use deep neural networks to automatically classify retinal fundus images into healthy and pathological categories. Due to the fact that deep learning is a superb machine learning method that has shown to be incredibly accurate when applied to computer vision difficulties. Convolutional neural networks (CNNs) were employed in our study to categorize retinal pictures according to their level of health.