KOMBINASI ARSITEKTUR U-NET VGG-19 DENGAN ATTENTION GATE DALAM SEGMENTASI SEMANTIK PEMBULUH DARAH ARTERI DAN VENA PADA CITRA RETINA

MAULANA, REFKY and Desiani, Anita and Irmeilyana, Irmeilyana (2025) KOMBINASI ARSITEKTUR U-NET VGG-19 DENGAN ATTENTION GATE DALAM SEGMENTASI SEMANTIK PEMBULUH DARAH ARTERI DAN VENA PADA CITRA RETINA. Undergraduate thesis, Sriwijaya University.

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Abstract

Retinal blood vessels are categorized into arteries and veins. Arteries and veins have distinct functions and characteristics. Separation of arteries and veins in retinal images by semantic segmentation is an important step in supporting the diagnosis process of various diseases. Differences in the characteristics of arteries and veins can indicate the presence of disorders in the retina. This research uses a combination of U-Net architecture with the addition of VGG-19 and Attention Gate to segment arterial and venous blood vessels in retinal images. VGG-19 is applied in all parts of the convolution block contained in the encoder section aimed at learning more complex images. Attention Gate is inserted in the skip connections to focus the model on relevant features. The results of the application of the proposed architecture resulted in average performance on accuracy, sensitivity, specificity, f1-score, and IoU are good in segmenting arterial and venous blood vessels with 98.61%, 81.72%, 91.34%, 82.43% 72.67%. The average performance on the background label shows that the accuracy, sensitivity, f1-score, and IoU values have achieved good performance above 90%, although the specificity is still at 75%. Meanwhile, on vein labels, accuracy and specificity show good performance with values above 90%. However, the performance on sensitivity, f1-score, and IoU is already quite good at above 70%. However, the arterial label is still low due to the relatively small size of arterial features and is difficult to recognize. It is necessary to improve this architecture to get sensitivity, f1-score, and IoU values above 90%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi Semantik, Retina, U-Net, VGG-19, Attention Gate
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:	Refky Maulana
Date Deposited:	30 Jan 2025 08:20
Last Modified:	30 Jan 2025 08:20
URI:	http://repository.unsri.ac.id/id/eprint/167335

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