The objective of this paper is to classify Multiple Sclerosis courses using features extracted from Magnetic Resonance Spectroscopic Imaging (MRSI) combined with brain tissue segmentations of gray matter, white matter, and lesions. To this purpose we trained several classifiers, ranging from simple (i.e. Linear Discriminant Analysis) to state-of-the-art (i.e. Convolutional Neural Networks). We investigate four binary classification tasks and report maximum values of Area Under receiver operating characteristic Curve between 68% and 95%. Our best results were found after training Support Vector Machines with gaussian kernel on MRSI features combined with brain tissue segmentation features.
CITATION STYLE
Ion-Mărgineanu, A., Kocevar, G., Stamile, C., Sima, D. M., Durand-Dubief, F., Van Huffel, S., & Sappey-Marinier, D. (2017). A comparison of machine learning approaches for classifying multiple sclerosis courses using MRSI and brain segmentations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10614 LNCS, pp. 643–651). Springer Verlag. https://doi.org/10.1007/978-3-319-68612-7_73
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