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Brain Imaging

Brain Imaging

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Related Experiment Video

Updated: Jan 15, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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

Shomukh Qari¹, Maha A Thafar¹

¹Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|October 16, 2025

Summary

This summary is machine-generated.

This study developed an AI framework using MaxViT for accurate stroke classification from CT scans, achieving 98% accuracy. This tool enhances early stroke diagnosis and patient care in emergency settings.

Keywords:

deep learning explainable AI hemorrhagic stroke ischemic stroke neuroimaging stroke classification transfer learning vision transformer

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Neurology Diagnostics

Background:

Stroke is a major cause of death and disability globally.
Rapid diagnosis is crucial for effective stroke treatment.
Computed tomography (CT) scans are vital in emergency stroke assessment.

Purpose of the Study:

To develop an AI framework for multiclass stroke classification (ischemic, hemorrhagic, no stroke) using CT images.
To evaluate the performance of MaxViT and other transformer architectures for stroke detection.
To enhance diagnostic transparency using explainable AI (XAI).

Main Methods:

Utilized MaxViT, a Vision Transformer (ViT)-based architecture, for stroke classification.
Compared MaxViT against other transformer variants (ViT, TNT, ConvNeXt).
Applied data augmentation and Grad-CAM++ for explainability.

Main Results:

MaxViT with augmentation achieved 98.00% accuracy and F1-score.
The AI framework outperformed baseline models in stroke classification.
Grad-CAM++ visualizations confirmed accurate identification of stroke-related regions.

Conclusions:

The AI framework offers a trustworthy tool for stroke diagnosis.
This technology can improve timely and optimal stroke diagnosis in emergency departments.
Facilitates integration of AI into clinical practice for better patient outcomes.