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<p>Stroke detection using artificial intelligence (AI) continues to show promise in advancing medical imaging diagnostics, particularly in improving accuracy and accelerating clinical decision-making. This study presents VINCE-NETv1, a hybrid deep learning framework that integrates Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) modules, and a meta-learning component to capture spatial, temporal, and global features from CT stroke images. Designed with architectural efficiency in mind, the model was trained and evaluated separately on three datasets: Near East University Hospital CT, the Core-Penumbra Acute Ischemic Stroke Dataset (CPAISD), and the Kaggle Brain Stroke CT dataset. To assess optimization performance, four optimizers-Adam, AdamW, Lookahead, and RMSProp-were used during training, each demonstrating high classification accuracy, with VINCE-NETv1 achieving up to 99.9% on CPAISD and ~ 100% on the Near East dataset. Generalization claims are limited strictly to datasets with confirmed patient-level separation-specifically the Near East and CPAISD datasets. The Kaggle dataset, lacking metadata for patient disambiguation, was treated solely as an exploratory benchmark. Preliminary interpretability was demonstrated using Grad-CAM visualizations, suggesting alignment with stroke-relevant regions. Approximate confidence intervals were computed from repeated runs to provide early insight into performance robustness, though formal statistical validation and k-fold cross-validation are planned in future work. While VINCE-NETv1 shows architectural potential for scalable deployment and clinical integration, current claims regarding generalization, real-time feasibility, and model interpretability are made cautiously with acknowledgment of existing limitations. Future research will address these aspects to evolve VINCE-NETv1 into a reliable and interpretable clinical decision-support tool.</p>

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A novel hybrid adaptive transformer framework with multihead self attention for stroke detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Selorm Adablanu,
  • Utpal Barman,
  • Dulumani Das

摘要

Stroke detection using artificial intelligence (AI) continues to show promise in advancing medical imaging diagnostics, particularly in improving accuracy and accelerating clinical decision-making. This study presents VINCE-NETv1, a hybrid deep learning framework that integrates Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) modules, and a meta-learning component to capture spatial, temporal, and global features from CT stroke images. Designed with architectural efficiency in mind, the model was trained and evaluated separately on three datasets: Near East University Hospital CT, the Core-Penumbra Acute Ischemic Stroke Dataset (CPAISD), and the Kaggle Brain Stroke CT dataset. To assess optimization performance, four optimizers-Adam, AdamW, Lookahead, and RMSProp-were used during training, each demonstrating high classification accuracy, with VINCE-NETv1 achieving up to 99.9% on CPAISD and ~ 100% on the Near East dataset. Generalization claims are limited strictly to datasets with confirmed patient-level separation-specifically the Near East and CPAISD datasets. The Kaggle dataset, lacking metadata for patient disambiguation, was treated solely as an exploratory benchmark. Preliminary interpretability was demonstrated using Grad-CAM visualizations, suggesting alignment with stroke-relevant regions. Approximate confidence intervals were computed from repeated runs to provide early insight into performance robustness, though formal statistical validation and k-fold cross-validation are planned in future work. While VINCE-NETv1 shows architectural potential for scalable deployment and clinical integration, current claims regarding generalization, real-time feasibility, and model interpretability are made cautiously with acknowledgment of existing limitations. Future research will address these aspects to evolve VINCE-NETv1 into a reliable and interpretable clinical decision-support tool.