Validated Swin Transformer-Based Deep Learning Pipeline with Cross-Validation and McNemar’s Test for Multi-Class Hemorrhage Classification in Traumatic Brain Injury CT Scans

Traumatic brain injury (TBI) remains a major global health concern, where rapid and accurate identification of intracranial hemorrhage on computed tomography (CT) scans is essential for improving patient outcomes. Manual radiological assessment, although clinically effective, is time-consuming and subject to inter-observer variability, creating a need for reliable automated diagnostic systems. Most existing artificial intelligence (AI) studies focus on binary hemorrhage detection or limited subtype classification, leaving a gap in robust multi-class intracranial hemorrhage categorization. This study presents a validated deep learning pipeline based on the Swin Transformer—a hierarchical Vision Transformer architecture that employs windowed and shifted multi-head self-attention to capture both local and global contextual information. A balanced dataset of 60,000 CT images across six clinically relevant classes was generated through structured augmentation of a publicly available source, ensuring fairness and improved generalization. The model was optimized using AdamW, cosine annealing scheduling, and label smoothing to enhance stability. The proposed framework achieved a classification accuracy of 82.02%, outperforming the strong baseline EfficientNetV2-S model (80.17%). Statistical significance was established using McNemar’s test (p < 0.0001), and robustness was demonstrated through 5-fold cross-validation. Gradient-weighted Class Activation Mapping (Grad-CAM)++- based interpretability analysis further confirmed that the model consistently highlighted clinically meaningful hemorrhagic regions, reinforcing its diagnostic relevance. These findings establish the Swin Transformer as an effective, interpretable, and statistically validated solution for multi-class intracranial hemorrhage classification, with strong potential to support radiologists in emergency triage and improve clinical decision-making.