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Updated: Jun 27, 2025

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

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Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.

Wafae Abbaoui^1,2, Sara Retal³, Soumia Ziti¹

¹Intelligent Processing & Security of Systems (IPSS) Research Team, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10000, Morocco.

Journal of Clinical Medicine

|April 27, 2024

Summary

The Vision Transformer (ViT-b16) model achieved 97.59% accuracy in classifying ischemic stroke from MRI scans, significantly outperforming the VGG-16 model. This highlights advanced AI for improved stroke diagnosis.

Keywords:

MRI scans deep learning ischemic stroke medical image analysis vision transformer

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Last Updated: Jun 27, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Evaluating deep learning models for medical image analysis.
Focusing on ischemic stroke classification using MRI scans.
Utilizing Moroccan patient data for model training and validation.

Purpose of the Study:

To assess the performance of the Vision Transformer (ViT-b16) model.
To compare ViT-b16 against the Visual Geometry Group 16 (VGG-16) model.
To determine the efficacy of ViT-b16 in classifying ischemic stroke from MRI scans.

Main Methods:

Dataset compilation of 342 MRI scans (Normal vs. Stroke).
Image preprocessing using TensorFlow's tf.data API.
Training and evaluation of the ViT-b16 model.

Main Results:

ViT-b16 achieved an accuracy of 97.59%.
ViT-b16 significantly outperformed the VGG-16 model (90% accuracy).
Demonstrated superior classification capabilities for ischemic stroke.

Conclusions:

ViT-b16 shows high potential for accurate ischemic stroke diagnosis.
Advanced deep learning models can enhance medical image analysis.
Supports further research into AI for improved clinical healthcare outcomes.