Brain Stroke Diagnosis Using Auxiliary Branch Guided Swin Transformer with Pseudo-Segmentation Supervision
Omarov B. Ikram Z.
6 October 2025Dr D. Pylarinos
Engineering, Technology and Applied Science Research
2025#15Issue 527312 - 27317 pp.
Accurate and timely diagnosis of brain stroke is critical for effective clinical intervention and long-term patient outcomes. In this study, a novel deep learning-based framework for automated stroke diagnosis is proposed, utilizing an Auxiliary Branch Guided Swin Transformer with pseudo-segmentation supervision. The proposed architecture combines the hierarchical representation power of the Swin Transformer with a parallel auxiliary segmentation branch to enhance lesion-specific attention and spatial awareness. To address the scarcity of detailed annotations in clinical datasets, we employ pseudo-labels generated from bounding box–level supervision, enabling the model to learn lesion localization without full pixel-wise segmentation masks. The model was trained and validated on the ISLES 2024 dataset, which includes multimodal brain MRI scans. Quantitative results demonstrate that the proposed model achieves 94.6% accuracy, 94.3% precision, 94% recall, and an F1-score of 94%, outperforming existing CNN-based and transformer-based approaches. The auxiliary branch not only facilitates better feature refinement but also improves generalization by promoting regularization during training. This study highlights the effectiveness of transformer-based architectures in medical image analysis and introduces a practical solution for weakly-supervised stroke detection, offering a promising tool for clinical decision support and automated neuroimaging diagnostics. (c) by the authors
auxiliary branch , brain MRI , deep learning , ISLES 2024 , pseudo-segmentation , stroke diagnosis , swin transformer
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Narxoz University, Kazakhstan
International Information Technology University, Kazakhstan
Kh. Dosmukhamedov Atyrau State University, Kazakhstan
Narxoz University
International Information Technology University
Kh. Dosmukhamedov Atyrau State University
10 лет помогаем публиковать статьи Международный издатель
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