SEGMENTASI LIVER MENGGUNAKAN KOMBINASI ARSITEKTUR DOUBLE U-NET RESIDUAL BLOCK DAN INCEPTION PADA CITRA 3D HASIL COMPUTED TOMOGRAPHY (CT) SCAN

FADHILAH, NADIYAH and Desiani, Anita and Amran, Ali (2024) SEGMENTASI LIVER MENGGUNAKAN KOMBINASI ARSITEKTUR DOUBLE U-NET RESIDUAL BLOCK DAN INCEPTION PADA CITRA 3D HASIL COMPUTED TOMOGRAPHY (CT) SCAN. Undergraduate thesis, Sriwijaya University.

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Abstract

Liver segmentation in abdominal CT-Scan images is carried out as an effort to detect disease in the liver organ. Liver segmentation can be done by utilizing deep learning with the Convolutional Neural Network (CNN) method. This research modifies the Double U-Net Residual Block and Inception architecture in liver segmentation on abdominal CT-Scan images. Modification of the Double U-Net architecture is done by replacing the encoder and decoder parts with Residual Block and replacing the bridge part with Inception. The results obtained accuracy, sensitivity, specificity, F1-Score, and Intersection over Union (IoU) values of 99.09%, 99.70%, 89.01%, 99.52%, and 99.04%, respectively. Based on the results, the double U-Net residual block and inception architecture is able to perform liver segmentation on abdominal CT-Scan images.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi, Liver, CT-Scan, Double U-Net
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Nadiyah Fadhilah
Date Deposited:	02 Jul 2024 02:19
Last Modified:	02 Jul 2024 02:19
URI:	http://repository.unsri.ac.id/id/eprint/148787

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