Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers
This addresses diagnosing diabetic retinopathy for diabetic patients, but it appears incremental as it uses standard methods without clear breakthroughs.
The paper tackled detecting diabetic retinopathy severity from fundus images by applying data preprocessing, segmentation, and an ensemble of classifiers, achieving unspecified results without concrete numbers.
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.