ArticleFrontiers in neuroinformatics2024
Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.
Article in Frontiers in neuroinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A Multilevel Deep Learning Model for Automated Brain Tumor Segmentation Using Magnetic Resonance Images.Diagnostics (Basel, Switzerland) · 2026Article
- BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.MethodsX · 2026Article
- CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.Scientific reports · 2026Article
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Advances in artificial intelligence and thermal analysis for brain tumor detection: a review of models, methods, and modalities.Frontiers in artificial intelligence · 2026Review
- Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.International journal of biomedical imaging · 2026Review
- A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.Frontiers in neuroinformatics · 2026Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN.Biomedicines · 2025Article
- Empirical Evaluation of Invariances in Deep Vision Models.Journal of imaging · 2025Article
- Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability.Scientific reports · 2025Article
- Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.Scientific reports · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Utilizing Vision Transformers for Predicting Early Response of Brain Metastasis to Magnetic Resonance Imaging-Guided Stage Gamma Knife Radiosurgery Treatment.Tomography (Ann Arbor, Mich.) · 2025Article
- Brain tumor classification using GAN-augmented data with autoencoders and Swin Transformers.Frontiers in medicine · 2025Article
- Efficient brain tumor grade classification using ensemble deep learning models.BMC medical imaging · 2024Article
- Integrating deep learning and thermal estimation for enhanced MRI-based brain tumor diagnosis.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans. Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification. Results: Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986. Conclusion: RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.
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