MultiScaleSegNet: A novel framework for multi-modal brain tumor segmentation

Accurate segmentation of brain tumors from multi-modal MRI is critical for diagnosis and treatment, but remains challenging due to heterogeneous tumor morphology, ambiguous boundaries, and the need to integrate both local details and global context. To address these challenges, we propose MultiScaleSegNet, a novel encoder–decoder framework that synergistically integrates a Swin Transformer encoder with a DenseNet-based decoder. Our model introduces three key components: (1) a Dual-Path Attention mechanism for feature extraction (DPA-MFE) that preserves spatial details while modeling long-range dependencies; (2) an Attention-based Feature Enhanced Network (AFENet) at the bottleneck to recalibrate features channel-wise and spatially; and (3) a Cross-Feature Refinement (CFR) block that expands the receptive field using dilated convolutions. The decoder further leverages CFR-refined skip connections to recover precise boundary information. Trained with a hybrid BCE-Dice loss on BraTS 2020 and BraTS 2021 datasets, our model achieves state-of-the-art performance, with average Dice scores of 0.972 and 0.987, respectively. Extensive experiments, including ablation studies and comparisons with existing methods, demonstrate that MultiScaleSegNet provides a robust and accurate solution for brain tumor segmentation, offering a strong foundation for clinical applications.