Processing Retinal Images to Discover Diseases

The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the most common retinal diseases are Diabetic Retinopathy, Glaucoma, and Cataract. The world is presently experiencing an epidemic of Diabetic Retinopathy (DR). Current predictions draw an estimation of doubling of the number affected from the current 170 million to an estimated 367 million by 2030. We propose a system wherein we extract blood vessels of the retina to detect eye diseases. Manually extracting the blood vessels of the human retina is a time-consuming task, and thus an automation of this process results in easy implementation of the work. This paper aims to design and consequently implement deep convolutional neural networks to identify the presence of an exudate, and thereby classify it into Diabetic Retinopathy, Glaucoma, and/or Cataract.