Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling

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 x 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 x 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.

Automated summary

This study investigated the ability of an ensemble of ViT models for the diagnosis of brain tumors from T1w MRI, showing superior performance of the ensemble ViT models with the best accuracy at 384 x 384 resolution.