Segmentation of the retinal vessels is extremely useful and very important in the diagnosis and management of various diseases associated with the eye, including diabetic retinopathy and glaucoma. The work has presented an improved methodology using an IS-Net model trained on the high-resolution FIVES dataset, including 800 annotated images of the retina. This paper therefore resolves the proposed approach by pre-processing, which consists of normalizing and performing horizontal flipping, followed by enhancement using IS-Net and histogram-based thresholding criteria for vessel structure binarization. The IS-Net architecture is designed with multi-scale RSU blocks to capture both fine and broad vessel details comprehensively for segmentation. Results have shown that IS-Net achieves a good balance in recall and specificity, with the F1 score high enough to outperform other models in terms of specificity by reducing false positives. These findings underlined the effectiveness of IS-Net for clinical applications and emphasized the value of high-resolution data for refinement in the performance of segmentation.