Recent advances in Glioma grade classification using machine and deep learning on MR data

Glioma grade classification based on Magnetic Resonance (MR) data and using Machine Learning approaches is a hot topic. Recently, considerable improvements have been made in this field especially in the last two years. This paper reviews a selection of the most recent methods from 2018 and 2019, details their preprocessing and priors, such as the different modalities used in their datasets. It then groups the different approaches by comparing their different learning scheme. While classical machine learning is present, more and more authors are using Convolutional Neural Networks. Multimodal MR sequencing, such as perfusion imaging, diffusion imaging or MR spectroscopy, gives an interesting diversity of information. Works using these modalities often reach an interesting accuracy level in classification.