Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003944	T148787	T1487872024	Central Library (References)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1487872024

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam., 2024

Collation

xi, 46 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Ilmu Matematika
CT-Scan

Specific Detail Info

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Statement of Responsibility

KA

Other version/related

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