ARSITEKTUR VOLUMETRIC U-NET DAN TRANSFORMER UNTUK SEGMENTASI TUMOR OTAK PADA CITRA HASIL MAGNETIC RESONANCE IMAGING

RAMADHAN, FAISHAL FITRA and Amran, Ali and Suprihatin, Bambang (2025) ARSITEKTUR VOLUMETRIC U-NET DAN TRANSFORMER UNTUK SEGMENTASI TUMOR OTAK PADA CITRA HASIL MAGNETIC RESONANCE IMAGING. Undergraduate thesis, Sriwijaya University.

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Abstract

Brain tumors are abnormal tissue growths within the brain that can lead to death. The components of a brain tumor can be classified into background, enhancing tumor, peritumoral edema, and non-enhancing tumor, which are identified in three-dimensional Magnetic Resonance Imaging (MRI) scans. The separation of these components can be achieved through automatic segmentation. This study proposes a combination of Vision Transformer (ViT) and Volumetric U-Net architectures for the segmentation of brain tumor components in MRI images. ViT is utilized in the encoder to capture global spatial relationships, while the decoder maintains the Volumetric U-Net structure to preserve local spatial details. The performance of the proposed architecture achieved accuracy, sensitivity, specificity, IoU, and f1-score values of 98.78%, 80.33%, 97.01%, 74.6%, and 83.3%, respectively. These results indicate a good performance in brain tumor segmentation from MRI images. At the label level, the model achieved accuracy, sensitivity, specificity, IoU, and f1-score values ranging from 78% to 98% for background, enhancing tumor, and peritumoral edema. However, the performance for the non-enhancing tumor label was relatively low, with an accuracy of 98.6%, sensitivity of 43.6%, specificity of 99.9%, IoU of 43.2%, and f1-score of 60.2%. This lower performance is attributed to the relatively small feature size and unclear boundaries of the non-enhancing tumor region. Based on these findings, future studies are encouraged to explore new approaches capable of better detecting small regions in 3D brain tumor segmentation.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi tumor otak, MRI, Volumentric U-Net, Vision Transformer, Deep Learning
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:	Faishal Firra Ramadhan
Date Deposited:	25 May 2025 03:14
Last Modified:	25 May 2025 03:14
URI:	http://repository.unsri.ac.id/id/eprint/174025

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