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Riko Arlando Saragih
Universitas Kristen Maranatha
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : Green Intelligent Systems and Applications

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Comparison of Convolutional Neural Network Model for Brain Tumor Disease Gliome Detection Santoso, Wulan Sallyndri; Saragih, Riko Arlando
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.899

Abstract

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.
Co-Authors Aan Darmawan Aan Darmawan Hangkawidjaja Aan Darmawan Hangkawidjaja Agus Prijono Anang Kristianto Daniel Setiadikarunia Elia Moses Erwani Merry Sartika Herawati Yusuf Herawati Yusuf Heri Andrianto Ikuthen Gabriel Barus Judea Janoto Jarden Mohamad Hafiz Gema Takbir Akbar Moses, Elia Novie Theresia Br. Pasaribu Novie Theresia Br. Pasaribu Oktafiandi, Aulia Rashif Ilmi Nurzaman Ratnadewi . Ratnadewi, Ratnadewi Roy Rikki Hutahean Ruben Stefanus Santoso, Wulan Sallyndri Sunoto, Tio Dewantho Tio Dewantho Sunoto Tio Dewantho Sunoto Wijaya, Kioe (Oeij) O. K. Ricky Yohana Susanthi Yosafat Aji Pranata