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August 25, 2025AIOpen Access

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

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Authors

SASerra AksoyPDPınar DemircioğluİBİsmail Böğrekçi

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Overview

Automated stroke classification shows 95% accuracy in CT brain imaging, suggesting effective diagnostic support in resource-poor settings.

Key Points

  • The AI system achieved 95% accuracy in classifying stroke types from CT brain images, confirming its diagnostic reliability.
  • Trained using 6653 CT scans, the model utilized ResNet-18 architecture and fine-tuning methods for enhanced performance.
  • Integrated with explainability features, the framework improves clinician trust and patient safety during stroke evaluation.
  • This system can significantly assist emergency rooms in accurately identifying strokes when specialist resources are limited.

Cite This Study

Aksoy et al. (2025) studied this question.

synapsesocial.com/papers/68af7df27567bf4f94ff4f4ehttps://doi.org/10.3390/ai6090202
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Brain Stroke Detection and Classification Using CT Imaging with Transformer Models and Explainable AI2025
  2. 2An Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography (CT) Images2025
  3. 3Advanced Deep Learning for Stroke Classification Using Multi-Slice CT Image Analysis2025 · 5 citations
  4. 4Applications of artificial intelligence in acute stroke imaging2025 · 2 citations
  5. 5An-AI-Driven Approach for Early Detection and Classification of Stroke Variants2025