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

Manyu Cui; Fei Li; Wei Yao; Guirong Peng

doi:10.15672/hujms.1659064

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Kaynak Göster

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

Yıl 2025, , 998 - 1020, 24.06.2025

Manyu Cui , Fei Li , Wei Yao , Guirong Peng

https://doi.org/10.15672/hujms.1659064

Öz

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.

Anahtar Kelimeler

Image processing, fuzzy rough sets, metric, fuzzy positive region, magnetic resonance imaging

Etik Beyan

This study does not involve human or animal experimentation, and the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Destekleyen Kurum

This research was funded by the National Natural science Foundation of China (Nos.12371462) and the Fundamental Research Funds for the Central Universities (No.2019zy20).

Proje Numarası

Nos.12371462; No.2019zy20

Teşekkür

We would like to thank the referees and the editor for their constructive suggestions.

Kaynakça

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