<![CDATA[Brain tumor segmentation is a critical task in medical image analysis, as accurate identification and delineation of tumor regions support timely diagnosis, treatment planning, therapeutic decision-making, and disease monitoring. Magnetic Resonance Imaging (MRI) provides multiple complementary modalities that offer valuable information about tumor structure and tissue characteristics. However, accurate segmentation remains challenging due to substantial variations in tumor size, shape, texture, and location, along with irregular and overlapping tumor boundaries, low-contrast regions, and considerable differences in the information captured by different MRI modalities. To address these challenges, this study proposes NeuroQFormer-Net (Quaternion-Guided Hybrid Attention Transformer Feature Fusion Network), a novel deep learning framework for multi-modal brain tumor segmentation. The proposed framework employs quaternion-based inter-channel correlation learning to effectively capture and integrate complementary information across multiple MRI modalities while preserving structural dependencies and enhancing cross-modal feature representations. In addition, a hybrid attention mechanism integrated with transformer-based learning is utilized to simultaneously capture fine-grained local structural characteristics and long-range contextual dependencies. To further improve segmentation accuracy, an adaptive co-learning feature fusion strategy is introduced to effectively integrate multi-scale representations obtained from different network levels. Furthermore, a boundary-aware refinement module enhances tumor boundary localization and reduces segmentation errors, particularly in low-contrast, irregular, and complex tumor regions. Experimental evaluation demonstrates the effectiveness of NeuroQFormer-Net, achieving an average Dice Similarity Coefficient (DSC) of 96.45%, Intersection over Union (IoU) of 93.26%, Precision of 96.17%, Recall of 96.08%, and Hausdorff Distance (HD) of 2.31 mm. These results indicate that the proposed framework provides accurate and robust multi-modal brain tumor segmentation]]>
