IMPLEMENTASI ARSITEKTUR UNET-TRANSFORMER DALAM SEGMENTASI SEMANTIK CITRA PEMBULUH DARAH ARTERI DAN VENA RETINA

GIOVILLANDO, GIOVILLANDO and Desiani, Anita and Irmeilyana, Irmeilyana (2025) IMPLEMENTASI ARSITEKTUR UNET-TRANSFORMER DALAM SEGMENTASI SEMANTIK CITRA PEMBULUH DARAH ARTERI DAN VENA RETINA. Undergraduate thesis, Sriwijaya University.

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Abstract

Retinal blood vessels are crucial components of the eye's circulatory system, functioning to supply oxygen and nutrients while removing waste from retinal tissues. Retinal blood vessels are divided into two types: arteries and veins (A/V). Arteries and veins are often located close to each other, necessitating clear separation to assist medical professionals in identifying specific diseases and preventing errors in analyzing related conditions. The separation of arteries and veins can be performed using image segmentation-based technology. This study aims to conduct semantic segmentation of retinal blood vessels by combining the U-Net architecture, Vision Transformer (ViT), and Attention Gate. The proposed model employs ViT as an encoder to capture global spatial relationships, while U-Net acts as a decoder to restore image spatial details. An Attention Gate is integrated to filter relevant information from generated features. Segmentation performance was evaluated across five label classes: Background, Artery, Crossings, Vein, and Uncertain, using metrics including accuracy, sensitivity, specificity, F1-Score, and IoU. Evaluation results indicate that the proposed model achieved an average accuracy of 99.13%, demonstrating its ability to classify pixels with high alignment to ground truth. A sensitivity of 80.78% is classified as good, reflecting adequate balance in detecting True Positives (TP). Specificity of 91.69% indicates excellent performance in identifying Background pixels or True Negatives (TN). An F1-Score of 78.64% shows the model's reasonable balance in performance across classes. An average IoU of 77.34% suggests the model has not yet reached optimal performance in predicting overlapping areas with ground truth for artery and vein labels. This study demonstrates that the combination of U-Net, Vision Transformer, and Attention Gate effectively enhances the performance of semantic segmentation for retinal blood vessels, though improvements are still needed for specific labels

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Attention Gate, Pembuluh Darah Retina, Segmentasi Semantik, U-Net, Vision Transformer
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Giovillando Giovillando
Date Deposited:	22 Mar 2025 01:09
Last Modified:	22 Mar 2025 01:09
URI:	http://repository.unsri.ac.id/id/eprint/169750

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