Diabetic Eye Disease Detection Using Machine Learning Techniques


Authors : Prof. Jayshree Aher; Araadhya Sharma; Sashank Vemulapalli

Volume/Issue : Volume 5 - 2020, Issue 6 - June

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/31OzyRH

Diabetic Retinopathy (DR) is an eye disease that affects people that suffer from diabetes over prolonged periods of time. If not detected and diagnosed at the right time, it often leads to weakening of vision and can even lead to absolute loss of vision. The disease generally affects people who are aged between 35 to 50 years, but recent cases involving teenagers have also been reported widely. The process for diagnosing Diabetic Retinopathy is often difficult since very few visible symptoms appear in patients until it is too late for treatment and the point of no return is met. Current techniques that exist for detecting Diabetic Retinopathy are extremely time consuming and require a manual procedure to be carried out by lab technicians which involves inserting medical tools into the patient’s eye. The proposed methodology is to utilize the neoteric branch of computer science i.e. Machine Learning techniques to assist in identifying and diagnosing the disease by analysing the images of the eye. As per the research study, the images will be preprocessed, and converted to the Gray Scale following which the extraction of relevant features using appropriate supervised learning techniques are carried out to obtain the final trained model.

Keywords : Diabetic Retinopathy, Diabetic eye disease, Microaneurysms, Exudates, Machine learning, Supervised learning Introduction

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