Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging

Chew-Wai Yap Et.Al

doi:10.17762/turcomat.v12i3.1023

Abstract

An application of auto-detecting Diabetic Retinopathy (DR) is indispensable to aid the ophthalmologists in diagnosing patients and also to help relevant organisations in accumulating and analysing data. This project presents DR Miner, an application that can extract data from fundus images, identify the symptoms of DR in retina images by using data science approaches, and collect the ophthalmologist’s review to improve the detection model in the future. To form the DR data set with binary classes, Auto Colour Correlogram (ACC) was utilised to extract the features from DR images. Over-sampling was then conducted to balance the class distribution in the data set. To reduce the variance of the single learning algorithms, we evaluated various bagging approaches. Theresults showed that the bagging approaches gave better results than the single learning algorithms in general. Out of all bagging approaches we evaluated, bagged k-nearest neighbours gave the best result. The sensitivity achieved was 85.1%, which met the requirement set by the UK National Institute for Clinical Excellence.

Highlights

The eyes are the visual system of the human being
The National Diabetes Registry (NDR) of the Ministry of Health Malaysia helps to gather medical data and perform data analyses, but it is very dependent on human to input information
The bagging approaches gave relatively better results than the single learning algorithms.The top performer is the bagged K-Nearest Neighbours, and it gave the best results in these four evaluation metrics, namely, sensitivity, specificity, accuracy and the Receiver Operating Characteristic (ROC)

Summary

Introduction

The eyes are the visual system of the human being. It is a sensitive, complex, and the weakest part of the human body. Diabetic Retinopathy (DR) is a diabetes hurdle that brings inconvenience to the eyes. It is a metabolic disease caused by the high level of blood sugar, which leads to eye damage over time (Who.Int, 2019). By the year 2030, the World Health Organisation estimates that about 2.48 million Malaysians would suffer from DM (MoH, 2017). DR is the most common cause of visual loss in Malaysia, ranked second after cataract. Automating DR detection leads to to a more efficient and cost-effective assessment and to provide a second opinion for the ophthalmologists. Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogram and Bagging methodological approach on how to develop the application in the Methodology Section. The results of utilising various data science approaches shall be discussed in the Results and Discussion Section, and this study shall be concluded in the Conclusion Section

The Problem of Clinical Information in Malaysia

The Problem of Decision Support in Malaysia

Reducing DR Grading Costs

Detecting DR using Data Science Approaches

Results and Discussion

Conclusion