This paper presents a comprehensive review of retinal blood vessel segmentation based on
deep learning. The geometric characteristics of retinal vessels reflect the health status of …

The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of
different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular …

Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in
modern ophthalmology. In this paper, we present a novel method to segment blood vessels …

In recent years, enormous research has been carried out on the segmentation of blood
vessels. Segmentation of blood vessels in retinal images is crucial for diagnosing, treating …

There is a close correlation between retinal vascular status and physical diseases such as
eye lesions. Retinal fundus images are an important basis for diagnosing diseases such as …

This study provides a comparative analysis of deep learning models—UNet, Res-UNet,
Attention Res-UNet, and nnUNet—evaluating their performance in brain tumour, polyp, and …

Medical imaging plays a crucial role in modern healthcare by providing non-invasive
visualisation of internal structures and abnormalities, enabling early disease detection …

Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration
(AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely …

Medical imaging plays a crucial role in modern healthcare by providing non-invasive
visualisation of internal structures and abnormalities, enabling early disease detection …

Retinal vessel segmentation, skeletonization, and the generation of vessel segments are
considered significant steps in any automated system for measuring the vessel biomarkers …