Diabetic Retinopathy Classification using Various Machine Learning Techniques: A Review

Diabetic Retinopathy Classification using Various Machine Learning Techniques: A Review
Author(s):
SR Ajitha, Dr G V Ramesh Babu
Keywords:
Diabetic Retinopathy, Convolutional Neural Networks, Fundus images
Abstract
Diabetes is common among those who have insulin issues. Only diabetics have diabetic retinopathy (DR), a devastating microvascular complication that destroys the retina and, if left misdiagnosed and untreated, can result in irreparable partial or whole blindness. In addition to the time it takes a patient to see an ophthalmologist who scans the patient's retina, many diabetics fail to recognize their illness and subsequently acquire visual impairments. Such human examination is time-consuming, slows the DR diagnosis method, allowing the illness to grow to more advanced stages within the window period, and is not always accurate. This article analyzes and evaluates the most recent research and survey articles addressing the precise diagnosis and classification of DR into distinct parts ranging from mild to severe.
Article Details
Unique Paper ID: 161577 Publication Volume & Issue: Volume 10, Issue 5 Page(s): 64 - 69
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