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Brain stroke detection and classification using deep learning-based transCBAMSegNet and hybrid transformer models.

Abdullah Şener1

  • 1Management Information Systems, Faculty of Economics and Administrative Sciences, Fırat University, Elazığ, 23100, Turkey. asener@firat.edu.tr.

Physical and Engineering Sciences in Medicine
|May 11, 2026
PubMed
Summary

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This study introduces a deep learning framework for automated stroke analysis on CT scans. The system accurately segments brain lesions and classifies stroke types, aiding rapid clinical decisions.

Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Stroke necessitates rapid assessment to minimize mortality and disability.
  • Accurate brain lesion segmentation and stroke type classification from CT scans are crucial for timely clinical decisions.

Purpose of the Study:

  • To develop a deep learning framework for automated stroke lesion segmentation and CT-based stroke type classification.
  • To create a computer-aided tool for early-stage, imaging-based stroke assessment.

Main Methods:

  • A deep learning framework incorporating TransCBAMSegNet for lesion segmentation.
  • A hybrid MaxxViT-ViT-SwinV2 feature fusion strategy for stroke type classification.
  • Utilized computed tomography (CT) imaging data for analysis.
Keywords:
Brain strokeComputed tomography imagingDeep learningHybrid transformerMedical image processingTransCBAMSegNet

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Main Results:

  • The TransCBAMSegNet model achieved 99.60% accuracy, 75.98% mIoU, and 75.50% DSS for segmentation.
  • The classification stage reached 97% accuracy in differentiating ischaemic stroke, haemorrhagic stroke, and normal cases.
  • The multi-transformer fusion approach outperformed single-backbone configurations.

Conclusions:

  • The proposed framework provides a robust and reproducible method for automated stroke analysis using CT imaging.
  • The developed tool can support early-stage clinical decision-making in stroke management.
  • Deep learning models demonstrate significant potential in enhancing stroke assessment accuracy and efficiency.