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This study is an extension of our previously published manuscript [2]. Building upon the foundations laid in our earlier work, this research further refines and extends the transformer-based approach to enhance its diagnostic capabilities. The focus remains on the integration of multi-modal information, combining both textual clinical narratives and imaging data from multi slice CT scans, to provide a more comprehensive and accurate diagnosis of brain strokes. In addition, we introduce a new ensemble learning approach based on logic gates. This innovative method combines information from clinical narratives and CT scans to enhance the diagnostic capabilities of our framework, marking a significant evolution in our approach to advancing precision in brain stroke detection.

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End-to-End Transformer Architecture with Novel Ensemble Learning Method Integrating CT Scans and Clinical Narratives for Brain Stroke Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Junaid Abdul Wahid,
  • Muhammad Ayoub,
  • Mingliang Xu,
  • Xiaoheng Jiang,
  • Shi Lei,
  • Lifeng Li,
  • Shabir Hussain,
  • Ashfaque Khawaja,
  • Yufei Gao

摘要

This study is an extension of our previously published manuscript [2]. Building upon the foundations laid in our earlier work, this research further refines and extends the transformer-based approach to enhance its diagnostic capabilities. The focus remains on the integration of multi-modal information, combining both textual clinical narratives and imaging data from multi slice CT scans, to provide a more comprehensive and accurate diagnosis of brain strokes. In addition, we introduce a new ensemble learning approach based on logic gates. This innovative method combines information from clinical narratives and CT scans to enhance the diagnostic capabilities of our framework, marking a significant evolution in our approach to advancing precision in brain stroke detection.