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This is a In Vitro Study study.

August 25, 2025AIOpen Access

An Explainable AI Framework for Stroke Classification Based on CT Brain Images

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Automated stroke classification reveals 95% accuracy in CT imaging, suggesting improved diagnostic assistance in healthcare settings.

The model achieved 95% test accuracy in classifying strokes from CT brain images and maintaining physician trust.
Key evidence shows the system effectively distinguished between hemorrhagic and ischemic strokes across diverse cases.
Employing a ResNet-18 architecture with transfer learning and explainability, the approach enhances clinical decision-making.
This framework highlights the importance of rapid diagnosis for effective stroke treatment, particularly in resource-poor environments.

Aksoy et al. (2025) studied this question.

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