Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks.

ABSTRACT: Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30

SUBMITTER: Marzullo A 

PROVIDER: S-EPMC6581753 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature