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This is a Quantitative Study study (also classified as: Experimental Study).

September 30, 2025DiagnosticsOpen Access

Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI

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This study demonstrates multiclass stroke classification using CT scans and explainable AI techniques.

The MaxViT model achieved an accuracy of 98.00% in classifying stroke types using CT scans.
Integration of Grad-CAM++ enhanced model transparency by providing visualizations of stroke-related areas.
Data augmentation techniques were employed to improve generalization and address class imbalance in stroke classification.
The research supports the use of AI in emergency departments for accurate and timely stroke diagnoses.

Qari et al. (2025) studied this question.

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