Machine learning approach for the identification of diabetes retinopathy and its stages

The effects of the eye abnormalities are mostly gradual in nature which shows the necessity for an accurate abnormality identification system. Abnormality in retina is one among them. Diabetic Retinopathy (DR) is a disease that causes damage to the retina of human eye, which is caused by complications of diabetes. DR is one of the main causes of vision loss and its prevalence keeps rising. Diabetic Retinopathy, a frequent diabetic retinal disease is caused due to the blood vessels in the retina get changes from its original shape. Diabetic Retinopathy generally affects both the human eyes. Most of the ophthalmologists depend on the visual interpretation for the identification of the types of diseases. But, inaccurate diagnosis will change the course of treatment planning which leads to fatal results. Hence, there is a requirement for a bias free automated system which yields highly accurate results. In this paper, we are classifying the various stages of DR. We first present a summary of diabetic retinopathy and its causes. Then, a literature review of the automatic detection of diabetic retinopathy techniques is presented. Explanation and restrictions of retina databases which are used to test the performance of these detection algorithms are given.

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