Abstract
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.
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Acknowledgments.
This work was funded by European project EU MC ITN TRANSACT 2012 (No. 316679) and the ERC Advanced Grant BIOTENSORS nr.339804. EU: The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013). This paper reflects only the authors’ views and the Union is not liable for any use that may be made of the contained information.
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Ion-Mărgineanu, A. et al. (2017). A Comparison of Machine Learning Approaches for Classifying Multiple Sclerosis Courses Using MRSI and Brain Segmentations. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_73
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DOI: https://doi.org/10.1007/978-3-319-68612-7_73
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