Machine Learning-Based Meningioma Location Classification Using Vision Transformers and Transfer Learning

PurposeIn this study, we aimed to use advanced machine learning (ML) techniques, specifically transfer learning and Vision Transformers (ViTs), to accurately classify meningioma in brain MRI scans. ViTs process images similarly to how humans visually perceive details and are useful for analyzing complex medical images. Transfer learning is a technique that uses models previously trained on large datasets and adapts them to specific use cases. Using transfer learning, this study aimed to enhance the diagnostic accuracy of meningioma location and demonstrate the capabilities the new technology. ApproachWe used a Google ViT model pre-trained on ImageNet-21k (a dataset with 14 million images and 21,843 classes) and fine-tuned on ImageNet 2012 (a dataset with 1 million images and 1,000 classes). Using this model, which was pre-trained and fine-tuned on large datasets of images, allowed us to leverage the predictive capabilities of the model trained on those large datasets without needing to train an entirely new model specific to only meningioma MRI scans. Transfer learning was used to adapt the pre-trained ViT to our specific use case, being meningioma location classification, using a dataset of 1,094 images of T1, contrast-enhanced, and T2-weighted MRI scans of meningiomas sorted according to location in the brain, with 11 different classes. ResultsThe final model trained and adapted on the meningioma MRI dataset achieved an average validation accuracy of 98.17% and a test accuracy of 89.95%. ConclusionsThis study demonstrates the potential of ViTs in meningioma location classification, leveraging their ability to analyze spatial relationships in medical images. While transfer learning enabled effective adaptation with limited data, class imbalance affected classification performance. Future work should focus on expanding datasets and incorporating ensemble learning to improve diagnostic reliability.