Preface

Retinal vessel segmentation plays a critical role in the early diagnosis of ophthalmic and neurological diseases, such as diabetic retinopathy and glaucoma.This paper presents a U-Net-based deep learning model for the automatic segmentation of retinal vessels.The model's performance is evaluated using various metrics, including the Dice coefficient, accuracy, precision, and recall.The experimental results demonstrate that the model effectively identifies larger vascular structures, while further fine-tuning is necessary to improve the detection of finer capillaries.The findings support the potential applicability of the model for medical diagnostic purposes.