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SEGMENTASI SEMANTIK PEMBULUH DARAH CITRA RETINA MENGGUNAKAN ARSITEKTUR KOMBINASI U-NET, DEEPLABV3+, DAN ATTENTION GATE

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Penilaian

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The blood vessels in the retina are a system of blood vessels that function as deliverers of oxygen and nutrients. The blood vessels of the retina are divided into two parts, namely arteries and veins. Abnormalities in both blood vessels can indicate various diseases in the retina. Abnormal blood vessels can be analyzed in digital retinal images with image segmentation. This research performs semantic segmentation that produces three features, namely arterial, venous, and background blood vessels. The segmentation architecture used in this research is a combination of U-Net, DeepLabV3+, and Attention gate architectures. The use of DeepLabV3+ in the decoder aims to generate fewer parameters and extract features without reducing image resolution. The addition of Attention gate to the skip connection in the U-Net encoder aims to select important and unimportant features. The average performance results on accuracy, sensitivity, specificity, f1-score, and IoU are good enough in segmenting 96.32%, 79.11%, 91.96%, 80.97%, and 69.86%. Labeling results on the background label show that the accuracy, specificity, f1-score, and IoU values are good enough to segment above 95%, while the specificity is still at 77%. On arteries and veins labeling, the accuracy and specificity values are good enough to segment above 95%. However, the performance values on sensitivity, f1-score, and IoU are still below 95%. This is because there are very few features in arteries and veins to perform segmentation, so improvements are needed in this architecture to get sensitivity, f1-score, and IoU values above 95%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507001057	T167323	T1673232025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1673232025

Publisher

: Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

xvii, 65 hlm.: Ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Matematika

Specific Detail Info

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Statement of Responsibility

EM

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