ArticleCurrent oncology (Toronto, Ont.)2022
Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling.
Article in Current oncology (Toronto, Ont.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 228 citations in OpenAlex.
- Cloud-Based Artificial Intelligence Classification of Common Intracranial Tumors on Magnetic Resonance Imaging.Cureus · 2026Article
- From Static Diagnosis to Dynamic Guidance : Evolution of Artificial Intelligence in Pediatric Neuroimaging.Journal of Korean Neurosurgical Society · 2026Article
- Vision-Language Models in Medical Imaging for Cancer Diagnosis: A Bibliometric Review.Bioengineering (Basel, Switzerland) · 2026Review
- Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations.Scientific reports · 2026Article
- Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.Biomedicines · 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
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- An Efficient and Cohesive System for Enhanced Accuracy in Malignant Brain Tumor Diagnosis.Current medical imaging · 2026Article
- FS-FCN: a frequency-spatial fusion convolutional network for brain tumor classification in MR images.Frontiers in neurology · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Hybrid segmentation model and CAViaR -based Xception Maxout network for brain tumor detection using MRI images.Medical & biological engineering & computing · 2025Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Fine-tuned ResNet34 for efficient brain tumor classification.Scientific reports · 2025Article
- A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.Scientific reports · 2025Article
- Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.Journal of imaging informatics in medicine · 2025Article
- Hybrid of VGG-16 and FTVT-b16 Models to Enhance Brain Tumors Classification Using MRI Images.Diagnostics (Basel, Switzerland) · 2025Article
- Sparse transformer and multipath decision tree: a novel approach for efficient brain tumor classification.Scientific reports · 2025Article
- A novel hybrid vision UNet architecture for brain tumor segmentation and classification.Scientific reports · 2025Article
- Deep learning based on ultrasound images to predict platinum resistance in patients with epithelial ovarian cancer.Biomedical engineering online · 2025Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model's ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model's ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.