SEMD-Net: A transformer based approach for brain tumor segmentation and classification

Abstract

Brain tumor segmentation and classification from MRI images are critical tasks in neuro-oncology, but they remain challenging due to tumor heterogeneity, ambiguous boundaries, and variability across multi-modal imaging sequences. Existing deep learning methods often struggle with subregion delineation and generalization, leading to incomplete or inaccurate results. In this paper, we propose Squeeze-and-Excitation Mamba with DeiSwin+ + (SEMD-Net), a unified deep learning framework designed to improve both tumor segmentation and imaging-based glioma grade classification (HGG vs. LGG). The model integrates a multi-branch learning architecture capable of capturing both global and localized tumor features. It combines spatial-channel attention mechanisms and transformer-based representation learning to address the challenges of boundary precision, tissue variability, and intra-class heterogeneity. We evaluated our method on the BraTS 2020 and 2021 datasets using five-fold cross-validation. SEMD-Net achieved strong performance on key segmentation metrics, including a mean Dice Similarity Coefficient (DSC) of 0.88 ± 0.02, IoU of 0.86 ± 0.03, and a Hausdorff Distance (HD) of 3.5 ± 0.8 mm. For glioma subtype classification, the model reached 97.5 % accuracy, 98.9 % precision, 98.8 % recall, and an F1-score of 98.85 %, outperforming benchmark methods such as U-Net, DeepSemantic, and AdaBoost. These results suggest that SEMD-Net effectively balances segmentation accuracy and classification robustness, offering a promising solution for integrated brain tumor analysis. While further validation on external datasets is ongoing, the proposed framework shows strong potential for clinical application in automated MRI-based diagnosis.