A hybrid swin transformer–BiLSTM framework and ensemble learning for multimodal brain stroke detection and risk prediction

Key Points

• The hybrid framework achieves significant improvements in stroke detection accuracy, with a notable increase in predictive performance.
• Key evidence shows that the method increases accuracy by 15% compared to traditional models in test scenarios.
• Assessment using novel algorithms, including a combination of swin transformer and BiLSTM, enhances data integration.
• Promising findings suggest that this approach could lead to more effective stroke management strategies in clinical settings.