ArticleFrontiers in oncology2024
A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- A Hybrid Approach for Brain-Tumor Detection and Classification from MRI Images.Journal of imaging · 2026Article
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- A functionally guided fusion Vision Transformer for predicting IDH status in gliomas: a multicenter study with external validation and incomplete multimodal evaluation.Radiologie (Heidelberg, Germany) · 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
- A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.Scientific reports · 2026Article
- Proto-memformer: deformable memory transformer for Parkinson's MRI classification.Scientific reports · 2026Article
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification.Cellular and molecular neurobiology · 2026Article
- Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- SMBO: a Sinusoidal Modulation Bi-population Optimizer for brain cancer MRI image clustering.Frontiers in neuroscience · 2026Article
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability.Frontiers in artificial intelligence · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.Physical and engineering sciences in medicine · 2025Article
- Hybrid of VGG-16 and FTVT-b16 Models to Enhance Brain Tumors Classification Using MRI Images.Diagnostics (Basel, Switzerland) · 2025Article
- Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.Scientific reports · 2025Article
- 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
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Abstract
Brain tumors occur due to the expansion of abnormal cell tissues and can be malignant (cancerous) or benign (not cancerous). Numerous factors such as the position, size, and progression rate are considered while detecting and diagnosing brain tumors. Detecting brain tumors in their initial phases is vital for diagnosis where MRI (magnetic resonance imaging) scans play an important role. Over the years, deep learning models have been extensively used for medical image processing. The current study primarily investigates the novel Fine-Tuned Vision Transformer models (FTVTs)-FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32-for brain tumor classification, while also comparing them with other established deep learning models such as ResNet50, MobileNet-V2, and EfficientNet - B0. A dataset with 7,023 images (MRI scans) categorized into four different classes, namely, glioma, meningioma, pituitary, and no tumor are used for classification. Further, the study presents a comparative analysis of these models including their accuracies and other evaluation metrics including recall, precision, and F1-score across each class. The deep learning models ResNet-50, EfficientNet-B0, and MobileNet-V2 obtained an accuracy of 96.5%, 95.1%, and 94.9%, respectively. Among all the FTVT models, FTVT-l16 model achieved a remarkable accuracy of 98.70% whereas other FTVT models FTVT-b16, FTVT-b32, and FTVT-132 achieved an accuracy of 98.09%, 96.87%, 98.62%, respectively, hence proving the efficacy and robustness of FTVT's in medical image processing.
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