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Journals / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 54 Issue: 3

Metric-based fuzzy rough sets for brain tumor magnetic resonance imaging classification

Manyu Cui(Beijing Forestry University)0009-0002-5512-8215
Fei Li(Beijing Forestry University)0009-0004-4434-8532
Wei Yao(Nanjing University of Information Science)0000-0003-3320-7609
Guirong Peng(Beijing Forestry University)0009-0004-0362-8693
Pages
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Publication Date
2025-06-24
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results demonstrate that the proposed algorithm significantly outperforms the other three methods in various evaluation metrics, including the confusion matrix, classification accuracy, and the kappa coefficient.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Computer-aided diagnosis systems help physicians diagnose diseases accurately at an early stage by automating preprocessing, image enhancement, and feature extraction, thus increasing patient survival rates.
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Key Findings & Data:Blocks with the highest fuzzy positive region values are selected for multiple enhancement rounds, forming a candidate set that is sequentially integrated back into the original image.
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Conclusion & Impact:The experimental results demonstrate that the proposed algorithm significantly outperforms the other three methods in various evaluation metrics, including the confusion matrix, classification accuracy, and the kappa coefficient.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Image processing · fuzzy rough sets · metric

Abstract

Computer-aided diagnosis systems help physicians diagnose diseases accurately at an early stage by automating preprocessing, image enhancement, and feature extraction, thus increasing patient survival rates. In this paper, we introduce an algorithm that leverages metric-based fuzzy positive regions to address the degradation of feature quality in brain tumor magnetic resonance imaging caused by inappropriate image enhancement. Employing sliding window blocks, the algorithm performs overlapping segmentation of magnetic resonance images and evaluates the membership of these blocks to decision classes by metric-based fuzzy positive regions. Blocks with the highest fuzzy positive region values are selected for multiple enhancement rounds, forming a candidate set that is sequentially integrated back into the original image. Finally, the features of the locally enhanced images are analyzed using the fuzzy positive region to generate the optimal feature set. To validate the effectiveness of the proposed algorithm, the features extracted using this method are compared with those extracted directly from the original image, globally enhanced images, and locally enhanced images processed based on similar fuzzy positive regions. The experimental results demonstrate that the proposed algorithm significantly outperforms the other three methods in various evaluation metrics, including the confusion matrix, classification accuracy, and the kappa coefficient.

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Cite This Article

Cui, M., Li, F., Yao, W., Peng, G. (2025). Metric-based fuzzy rough sets for brain tumor magnetic resonance imaging classification. Hacettepe Journal of Mathematics and Statistics, Volume: 54 Issue: 3. https://doi.org/10.15672/hujms.1659064

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