SEGMENTASI TUMOR OTAK MENGGUNAKAN ARSITEKTUR 3D DENSE-INCEPTION U-NET PADA CITRA HASIL MAGNETIC RESONANCE IMAGING OTAK

FRAMESTI, LIZAH and Suprihatin, Bambang and Hanum, Herlina (2025) SEGMENTASI TUMOR OTAK MENGGUNAKAN ARSITEKTUR 3D DENSE-INCEPTION U-NET PADA CITRA HASIL MAGNETIC RESONANCE IMAGING OTAK. Undergraduate thesis, Sriwijaya University.

Preview	Image RAMA_44201_08011382126088_cover.jpg - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (410kB) | Preview
	Text RAMA_44201_08011382126088.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (9MB) | Request a copy
	Text RAMA_44201_08011382126088_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (8MB) | Request a copy
	Text RAMA_44201_08011382126088_0026017102_0008016503_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (3MB)
	Text RAMA_44201_08011382126088_0026017102_0008016503_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_08011382126088_0026017102_0008016503_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (485kB) | Request a copy
	Text RAMA_44201_08011382126088_0026017102_0008016503_04.pdf - Accepted Version Restricted to Repository staff only Download (4MB) | Request a copy
	Text RAMA_44201_08011382126088_0026017102_0008016503_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (136kB) | Request a copy
	Text RAMA_44201_08011382126088_0026017102_0008016503_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy

Abstract

Brain tumor is a condition of abnormal growth of brain cells that can lead to death. Early detection is important for tumor patterns in Magnetic Resonance Imaging (MRI) images. Early detection is done by separating tumor cell details through 3D image segmentation with labels of background, non-enhancing tumor, peritumeral edema, and enhancing tumor. 3D image segmentation can be done automatically by utilizing the Convolutional Neural Network (CNN) method. The CNN architecture that is often used in 3D image segmentation is the 3D U-Net architecture. In the 3D U-Net encoder, the feature extraction process consists of a convolution block and a downsampling process. Downsampling reduces the spatial dimension which causes loss of detail information and vanishing gradient. This research proposes a modified 3D U-Net architecture with the addition of Dense Block and Block Inception in the encoder part to overcome vanishing gradient without increasing the number of parameters. The average performance results on accuracy, sensitivity, specificity, IoU, and f1-score with respective values of 99.05%, 93.37%, 98.69%, 87.7%, 92.4%. The results obtained accuracy performance on each label reached more than 95%, indicating the architecture was able to perform segmentation very well. Sensitivity and f1-score values on tumor enhancing labels are still below 90%, which means the model is still limited in detecting these parts optimally. The IoU value on the background label reaches more than 90%, while the non-enhancing tumor, peritumeral edema, and enhancing tumor labels are still below 90%, indicating that the tumor area prediction is smaller than the ground truth. The results of this study show that the 3D Dense-Inception U-Net architecture is able to perform segmentation well. However, IoU enhancement is needed to improve the quality of brain tumor segmentation.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi, Tumor Otak, Citra MRI, 3D U-Net, Dense-Inception
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA1.T553 Mathematics--Periodicals. Computer science--Periodicals. Computer science. 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:	Lizah Framesti
Date Deposited:	22 Mar 2025 01:18
Last Modified:	22 Mar 2025 01:18
URI:	http://repository.unsri.ac.id/id/eprint/169758

Actions (login required)

View Item