Vision Transformers for Brain Tumor Classification

With the increasing amount of data gathered by healthcare providers, interest has been growing in Machine Learning, and more specifically in Deep Learning. Medical applications of machine learning range from the prediction of medical events, to computer-aided detection, diagnosis, and classification. This paper will investigate the application of State-of-the-Art (SoA) Deep Neural Networks in classifying brain tumors. We distinguish between several types of brain tumors, which are typically diagnosed and classified by experts using Magnetic Resonance Imaging (MRI). The most common benign tumors are gliomas and meningiomas, however there exist many more which vary in size and location. Convolutional Neural Networks (CNN) are the SoA deep learning technique for image processing tasks such as image segmentation and classification. However, a recently developed architecture for image classification, namely Vision Transformers, have been shown to outperform classical CNNs in efficiency. while requiring fewer computational resources. This work introduces using only Transformer networks in brain tumor classification for the first time, and compares their performance with CNNs. A significant difference between the two models, tested in this manner, is the lack of translational equivariance in Transformers, which the CNNs already have. Experiments for brain tumor classification on benchmark real-world datasets show they can achieve comparable or better performance, despite using limited training data.