SEGMENTASI TUMOR OTAK PADA CITRA HASIL MAGNETIC RESONANCE IMAGING OTAK MENGGUNAKAN ARSITEKTUR 3D DOUBLE V-NET DENGAN ATTENTION GATE

YANTI, DINA ELLY and Suprihatin, Bambang and Amran, Ali (2025) SEGMENTASI TUMOR OTAK PADA CITRA HASIL MAGNETIC RESONANCE IMAGING OTAK MENGGUNAKAN ARSITEKTUR 3D DOUBLE V-NET DENGAN ATTENTION GATE. Undergraduate thesis, Sriwijaya University.

Preview	Image RAMA_44201_08011282126034_cover.jpg - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (135kB) | Preview
	Text RAMA_44201_08011282126034.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (11MB) | Request a copy
	Text RAMA_44201_08011282126034_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (9MB) | Request a copy
	Text RAMA_44201_08011282126034_0026017102_0013126603_01_font_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (3MB)
	Text RAMA_44201_08011282126034_0026017102_0013126603_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_44201_08011282126034_0026017102_0013126603_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (543kB) | Request a copy
	Text RAMA_44201_08011282126034_0026017102_0013126603_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_44201_08011282126034_0026017102_0013126603_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (180kB) | Request a copy
	Text RAMA_44201_08011282126034_0026017102_0013126603_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy

Abstract

Brain tumors are disorders caused by abnormal cell growth in the brain. Early detection is important to recognize tumor patterns in Magnetic Resonance Imaging (MRI) images. Early detection is done by separating tumor cell details through segmentation using the Convolutional Neural Network (CNN) method. This study proposes a 3D Double V-Net architecture by incorporating attention gates in each decoder within the skip connections. The 3D Double V-Net is a modified version of the 3D Double U-Net, where the bridge component is removed. This removal aims to reduce the number of parameters that could lead to overfitting. Attention gates are introduced in each decoder to refine feature selection from the skip connections. The proposed architecture is applied to brain tumor segmentation in three-dimensional MRI scans, which contain length, width, and depth information. The segmentation process is conducted on four labels: background, non-enhancing tumor, peritumoral edema, and enhancing tumor. The model's performance is evaluated using accuracy, sensitivity, specificity, IoU and F1-score. The implementation of the proposed architecture resulted in average performance on accuracy, sensitivity, specificity, IoU, and f1-score of 99.4%, 92.35%, 97.78%, 86.6%, and 92.67%. The model’s performance on the background label showed excellent results, with accuracy, sensitivity, specificity, IoU, and F1-score above 90%. The non-enhancing tumor label also demonstrated excellent performance, with accuracy, sensitivity, specificity, and F1-score above 90%, although the IoU value remained below 90%. The peritumoral edema and enhancing tumor labels showed excellent performance with accuracy, specificity, and F1-score above 90%, but sensitivity and IoU were still below 90%. This is due to the fact that the size of the features in the label enhancing tumor and peritumoral edema is relatively small and the edge boundaries are not clear, making it difficult to recognize. Based on this, architectural improvements are needed to increase sensitivity and IoU values above 90%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi, Tumor Otak, Citra MRI, 3D Double V-Net, Attention Gate
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5 Mathematics--Periodicals. Computer engineering. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45.B53 Big data. Cloud computing. Wireless sensor networks.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Dina Elly Yanti
Date Deposited:	21 Mar 2025 08:17
Last Modified:	21 Mar 2025 08:17
URI:	http://repository.unsri.ac.id/id/eprint/169733

Actions (login required)

View Item