DSC-SwinNet: A Dual-Stage Transformer Framework for Reliable Brain Tumor Segmentation and Classification from Multi-Modal MRI

The earlier diagnosis of brain tumors is a critical challenge that influences treatment and facilitates prompt detection of the disease. Conventional MRI provides a structural and functional view of the tumors. On the other hand, recent deep learning-based algorithms, particularly single-stage convolutional neural network-based models, face challenges in providing the exact location of the tumor as well as in enhancing detection and classification accuracy. This is due to a lack of global-local integration of features, lack of spatial consistency, and low resistance to intensity variation, which are typical of clinical MRI scans. In order to address these gaps, the proposed research uses the DSC-SwinNet…