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All Journal Recursive Journal of Informatics
Fariska Ratna Fauziah
Universitas Negeri Semarang
Computer Science & IT

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Optimization of Brain Tumor Segmentation on Magnetic Resonance Imaging (MRI) Using Attention Gate U-Net Fariska Ratna Fauziah; Budi Prasetiyo
Recursive Journal of Informatics Vol. 4 No. 1 (2026): March 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v4i1.27152

Abstract

Abstract. Brain tumor segmentation using Magnetic Resonance Imaging (MRI) plays a vital role in medical diagnosis, requiring high precision to support clinical decisions and reduce mortality rates.  Purpose: This research aims to enhance the segmentation process by implementing an Attention Gate into the U-Net model. Methods/Study design/approach: In the segmentation stage, Attention Gate on U-Net is integrated to filter out relevant information from the extracted features, resulting in a more precise segmentation of the brain tumor to determine the location of the tumor.  Result/Findings: The performance of the model is assessed by calculating several evaluation metrics such as dice coefficient and intersection-over-union (IoU) for the segmentation process. The results showed that adding Attention Gate to the U-Net achieved a dice coefficient of 87.08% and IoU of 72.70% Novelty/Originality/Value: The novelty of this study lies in the integration of the Attention Gate mechanism within the U-Net decoder stage to enhance focus on tumor regions. While U-Net is widely used in medical image segmentation, this specific attention-based enhancement significantly improves performance compared to conventional U-Net models without attention. This research contributes to advancing more accurate and efficient decision-support systems in the field of medical image analysis.
Co-Authors Budi Prasetiyo