Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers

TL;DR

This paper presents an ensemble classifier approach combined with image preprocessing and segmentation techniques to accurately detect and assess the severity of diabetic retinopathy from fundus images.

Contribution

It introduces a novel combination of preprocessing, segmentation, and ensemble classification specifically for diabetic retinopathy severity detection.

Findings

Effective detection of diabetic retinopathy severity levels.

Improved accuracy through ensemble classifier approach.

Robustness achieved via comprehensive image preprocessing.

Abstract

Diabetic retinopathy is an ocular condition that affects individuals with diabetes mellitus. It is a common complication of diabetes that can impact the eyes and lead to vision loss. One method for diagnosing diabetic retinopathy is the examination of the fundus of the eye. An ophthalmologist examines the back part of the eye, including the retina, optic nerve, and the blood vessels that supply the retina. In the case of diabetic retinopathy, the blood vessels in the retina deteriorate and can lead to bleeding, swelling, and other changes that affect vision. We proposed a method for detecting diabetic diabetic severity levels. First, a set of data-prerpocessing is applied to available data: adaptive equalisation, color normalisation, Gaussian filter, removal of the optic disc and blood vessels. Second, we perform image segmentation for relevant markers and extract features from the fundus images. Third, we apply an ensemble of classifiers and we assess the trust in the system.