Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques

Version 1 : Received: 4 November 2021 / Approved: 8 November 2021 / Online: 8 November 2021 (14:59:13 CET)

How to cite: Cifci, M.A. Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques. Preprints 2021, 2021110153 (doi: 10.20944/preprints202111.0153.v1). Cifci, M.A. Diagnosis of Diabetic Retinopathy Using Machine Learning Techniques. Preprints 2021, 2021110153 (doi: 10.20944/preprints202111.0153.v1).

Abstract

The complication of people with diabetes causes an illness known as Diabetic Retinopathy (DR). It is very widespread among middle-aged and older people. As diabetes progresses, patients' vision may deteriorate and cause DR. People to lose their vision because of this illness. To cope with DR, early detection is needed. Patients will have to be checked by doctors regularly, which is a waste of time and energy. DR can be divided into two groups: non-proliferative (NPDR) while the other is proliferative (PDR). In this study, machine learning (ML) techniques are used to diagnose DR early. These are PNN, SVM, Bayesian Classification, and K-Means Clustering. These techniques will be evaluated and compared with each other to choose the best methodology. A total of 300 fundus photographs are processed for training and testing. The features are extracted from these raw images using image processing techniques. After an experiment, it is concluded that PNN has an accuracy of about 89%, Bayes Classifications 94%, SVM 97%, and K-Means Clustering 87%. The preliminary results prove that SVM is the best technique for early detection of DR.

Keywords

Diabetic Retinopathy; Fundus Images; Retina,; Support vector machine; K-Means Clustering.

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.