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Article | Green Intelligent Systems and Applications | Volume 6 - Issue 1 - 2026 | 51−64 | https://doi.org/10.53623/gisa.v6i1.899
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Comparison of Convolutional Neural Network Model for Brain Tumor Disease Gliome Detection

Author(s): Wulan Sallyndri Santoso , Riko Arlando Saragih ORCID https://orcid.org/0000-0003-2617-9363
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Electrical Engineering Department, Universitas Kristen Maranatha, Bandung, Indonesia

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Green Intelligent Systems and Applications

Volume 6 - Issue 1 - 2026

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Glioma represented one of the most aggressive forms of malignant brain tumors, necessitating early detection to optimize therapeutic intervention outcomes. Manual tumor identification through Magnetic Resonance Imaging (MRI) was labor-intensive and was susceptible to subjective interpretation errors. This study aimed to compare the performance of two Convolutional Neural Network (CNN) architectures, specifically Residual Network (ResNet) and U-Net, for glioma tumor detection in T2-weighted MRI sequences. The datasets employed were obtained from the BraTS and Kaggle repositories and underwent comprehensive preprocessing procedures, including normalization, augmentation, and conversion to Portable Network Graphics (PNG) format. The evaluation metrics demonstrated that the U-Net architecture exhibited superior performance compared to ResNet-18, achieving an accuracy of 88.16%, sensitivity of 80.00%, specificity of 88.43%, and F1-score of 68.97%. Conversely, ResNet-18 yielded an accuracy of 71.43%, sensitivity of 73.52%, specificity of 81.54%, and an F1-score of 70.14%. These findings indicated that U-Net demonstrated greater efficacy in recognizing tumor morphology within MRI data and preserving spatial information through its inherent skip connection mechanism. This investigation demonstrated the potential of the U-Net architecture to facilitate automated and enhanced accuracy in glioma detection, although further refinement was required to improve segmentation precision and clinical applicability.

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SUBMITTED: 16 November 2025
ACCEPTED: 24 February 2026
PUBLISHED: 5 March 2026
SUBMITTED to ACCEPTED: 100 days
DOI: https://doi.org/10.53623/gisa.v6i1.899

Cite this article
Santoso, W. S. ., & Saragih, R. A. (2026). Comparison of Convolutional Neural Network Model for Brain Tumor Disease Gliome Detection. Green Intelligent Systems and Applications, 6(1), 51−64. https://doi.org/10.53623/gisa.v6i1.899
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