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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Sistem : Jurnal Ilmu-Ilmu Teknik SMARTICS Journal Jurnal Restikom : Riset Teknik Informatika dan Komputer JiTEKH (Jurnal Ilmiah Teknologi Harapan) Rambideun : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Polinema (JIP) Journix: Journal of Informatics and Computing
Putra, Firnanda Al Islama Achyunda
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences

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Journal : Journix: Journal of Informatics and Computing

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FCM-Guided CNN with Fuzzy Membership Maps for Robust Brain MRI Tumor Classification Firnanda Al-Islama Achyunda Putra; Kukuh Yudhistiro; Sutriawan; Zumhur Alamin
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.9

Abstract

Accurate brain MRI classification is critical for early tumor diagnosis and computer-aided clinical decision support. Conventional convolutional neural networks (CNNs) are effective in learning deep hierarchical features but often struggle with intensity heterogeneity and partial volume effects inherent to MRI data. To address these limitations, this study proposes a hybrid Fuzzy C-Means–CNN (FCM–CNN) framework that integrates unsupervised soft clustering with deep feature learning. The fuzzy segmentation stage preserves boundary uncertainty by generating multi-channel membership maps, which are then fed into a CNN for robust classification. Evaluations conducted on the Kaggle brain MRI dataset (3,264 slices across four diagnostic categories) under Stratified 5-Fold Cross-Validation show consistent improvements over baseline models. The proposed FCM–CNN achieves a mean accuracy of 96.26% and Macro-F1 of 0.9622, surpassing both CNN-only and K-Means+CNN by +4.84% and +2.74% respectively. Ablation analysis confirms that soft memberships enhance discrimination between visually similar tumors, while statistical testing verifies that the gains are systematic and reproducible. Furthermore, the fuzzy membership maps provide interpretable visual cues, aligning with recent trends in explainable AI (XAI) for medical imaging. Overall, the FCM–CNN framework demonstrates that combining fuzzy logic with deep learning yields a balanced trade-off between performance, interpretability, and computational efficiency, making it promising for clinical-grade brain MRI analysis.
Co-Authors Aditya Galih Sulaksono Ahmad Rizal Khamdani Andriyan Rizki Jatmiko As’ad Shidqy Aziz Karyudi, Mochammad Daffa Putra Kukuh Yudhistiro, Kukuh Listanto Tri Utomo Muhamat Ilham Ardiansa Ardiansa Nurul Hidayat Pusaka, Semerdanta Rizza Muhammad Arief Sutriawan Sutriawan Tri Afirianto Utaminingrum, Fitri Wayan Firdaus Mahmudy Yosta Yoserizal, Yosta Yulia Natasya Farah Diba Arifin Zumhur Alamin