Comparative Analysis of CNNs and Vision Transformer Models for Brain Tumor Detection

Safa Jraba, Mohamed Elleuch, Hela Ltifi, Monji Kherallah

2025

Abstract

Brain tumors are irregular cell mixtures existing within the brain or central spinal canal. They could be cancerous or benign. The likelihood of the best possible prognosis and therapy increases with the speed and accuracy of detection. This work provides a method for detecting brain tumors that combines the capabilities of vision transformers and CNNs. In contrast to other studies that primarily relied on standalone CNN or ViT architectures, our method uniquely integrates these models with a Support Vector Machine classifier for the improvement of accuracy and robustness in medical image classification. While the ViT makes it possible to combine CNN and ViT to improve the accuracy of medical imaging of the disease, the CNN extracts hierarchical features. In-depth analyses of benchmark datasets pertaining to imaging modalities and clinical perspectives were conducted. According to the experimental findings, ViT and EfficientNet identified tumors with an accuracy of 98%, while the greatest reported accuracy of 98.3% was obtained when ViT was combined with an SVM classifier. Our findings suggest that our method may improve brain tumor detection methods.

Paper Citation

in Harvard Style

Jraba S., Elleuch M., Ltifi H. and Kherallah M. (2025). Comparative Analysis of CNNs and Vision Transformer Models for Brain Tumor Detection. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1432-1439. DOI: 10.5220/0013381900003890

in Bibtex Style

@conference{icaart25,
author={Safa Jraba and Mohamed Elleuch and Hela Ltifi and Monji Kherallah},
title={Comparative Analysis of CNNs and Vision Transformer Models for Brain Tumor Detection},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1432-1439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013381900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Comparative Analysis of CNNs and Vision Transformer Models for Brain Tumor Detection
SN - 978-989-758-737-5
AU - Jraba S.
AU - Elleuch M.
AU - Ltifi H.
AU - Kherallah M.
PY - 2025
SP - 1432
EP - 1439
DO - 10.5220/0013381900003890
PB - SciTePress