Early Blindness Detection Based on Retinal Images Using Ensemble Learning

TL;DR

This paper proposes a novel ensemble learning-based method for early detection of blindness caused by diabetic retinopathy using retinal images, achieving 91% accuracy on rural South Asian data.

Contribution

It introduces a new ensemble learning approach utilizing color features from retinal images for early blindness detection, addressing image quality issues.

Findings

Achieved 91% classification accuracy.

Effective detection in rural South Asian populations.

Demonstrated robustness despite image quality challenges.

Abstract

Diabetic retinopathy (DR) is the primary cause of vision loss among grownup people around the world. In four out of five cases having diabetes for a prolonged period leads to DR. If detected early, more than 90 percent of the new DR occurrences can be prevented from turning into blindness through proper treatment. Despite having multiple treatment procedures available that are well capable to deal with DR, the negligence and failure of early detection cost most of the DR patients their precious eyesight. The recent developments in the field of Digital Image Processing (DIP) and Machine Learning (ML) have paved the way to use machines in this regard. The contemporary technologies allow us to develop devices capable of automatically detecting the condition of a persons eyes based on their retinal images. However, in practice, several factors hinder the quality of the captured images and impede the detection outcome. In this study, a novel early blind detection method has been proposed based on the color information extracted from retinal images using an ensemble learning algorithm. The method has been tested on a set of retinal images collected from people living in the rural areas of South Asia, which resulted in a 91 percent classification accuracy.