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Abstract Accurate early diagnosis of stroke is critical for reducing mortality and disability. Leveraging the rapid advances in deep learning, automated stroke detection has become an active research focus. Yet, existing approaches still underperform in detecting small lesions, adapting to complex backgrounds, and operating in real time.
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