A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features

TL;DR

This paper introduces a novel segmentation-based vascular enhancement technique combined with deep learning features, achieving near-perfect accuracy in multiclass retinal disease diagnosis from fundus images.

Contribution

It proposes Segmentation-based Vascular Enhancement (SVE) and a combined deep learning and traditional feature approach for improved retinal disease classification.

Findings

Achieved 99.96% accuracy on the STARE database

Proposed UNet-SVE-VGG-MLP model outperforms existing methods

Demonstrated rapid, objective, and accurate diagnosis capability

Abstract

Abnormalities in retinal fundus images may indicate certain pathologies such as diabetic retinopathy, hypertension, stroke, glaucoma, retinal macular edema, venous occlusion, and atherosclerosis, making the study and analysis of retinal images of great significance. In conventional medicine, the diagnosis of retina-related diseases relies on a physician's subjective assessment of the retinal fundus images, which is a time-consuming process and the accuracy is highly dependent on the physician's subjective experience. To this end, this paper proposes a fast, objective, and accurate method for the diagnosis of diseases related to retinal fundus images. This method is a multiclassification study of normal samples and 13 categories of disease samples on the STARE database, with a test set accuracy of 99.96%. Compared with other studies, our method achieved the highest accuracy. This study innovatively propose Segmentation-based Vascular Enhancement(SVE). After comparing the classification performances of the deep learning models of SVE images, original images and Smooth Grad-CAM ++ images, we extracted the deep learning features and traditional features of the SVE images and input them into nine meta learners for classification. The results shows that our proposed UNet-SVE-VGG-MLP model has the optimal performance for classifying diseases related to retinal fundus images on the STARE database, with a overall accuracy of 99.96% and a weighted AUC of 99.98% for the 14 categories on test dataset. This method can be used to realize rapid, objective, and accurate classification and diagnosis of retinal fundus image related diseases.