Advanced Deep Learning for Stroke Classification Using Multi-Slice CT Image Analysis

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Novel hybrid architecture improves stroke detection accuracy in CT imaging, indicating enhanced clinical scalability.

MedHybridNet achieves an accuracy of 98.31% in stroke classification, demonstrating its effectiveness in clinical scenarios.
The proposed SliceAttention mechanism enhances interpretability and eliminates the need for detailed slice-level annotations.
This study employs a hybrid model combining CNNs and Transformers, indicating a significant step in deep learning applications for medical imaging.
Results suggest that integrating AI into stroke care could lead to improved diagnostic efficiency and better patient outcomes.

Lezzar et al. (2025) studied this question.

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