ArticleSensors (Basel, Switzerland)2023
Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- A novel hybrid transformer-based framework (H-ConvNeXt-Swin) to classify brain tumors using MRI.Scientific reports · 2026Article
- A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.Scientific reports · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- CMRA-DETR: a lightweight and high-accuracy detection framework for MRI-based brain tumor identification.Frontiers in medicine · 2026Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024.Neuroradiology · 2025Review
- A novel hybrid vision UNet architecture for brain tumor segmentation and classification.Scientific reports · 2025Article
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- A novel approach for the detection of brain tumor and its classification via end-to-end vision transformer - CNN architecture.Frontiers in oncology · 2025Article
- Article
- Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach.Journal of clinical medicine · 2024Article
- A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery.Frontiers in oncology · 2024Article
- Artificial intelligence pipeline predicts the integrated molecular-morphologic risk score of meningiomas from routine preoperative MRI.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.
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