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

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.