Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features

• URL: http://arxiv.org/abs/2202.13853v1
• Date: Mon, 28 Feb 2022 15:09:42 GMT
• Title: Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features
• Authors: Rafael Rodrigues, Susana Quijano-Roy, Robert-Yves Carlier, and Antonio M. G. Pinheiro
• Abstract summary: Three methods are proposed to classify target muscles in Collagen VI-related myopathy cases. The best results were obtained with the hybrid model, resulting in a global accuracy of 93.8%.
• Score: 0.34998703934432684
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Magnetic Resonance Imaging (MRI) is a non-invasive tool for the clinical assessment of low-prevalence neuromuscular disorders. Automated diagnosis methods might reduce the need for biopsies and provide valuable information on disease follow-up. In this paper, three methods are proposed to classify target muscles in Collagen VI-related myopathy cases, based on their degree of involvement, notably a Convolutional Neural Network, a Fully Connected Network to classify texture features, and a hybrid method combining the two feature sets. The proposed methods was evaluated on axial T1-weighted Turbo Spin-Echo MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy or Bethlem Myopathy patients at different evolution stages. The best results were obtained with the hybrid model, resulting in a global accuracy of 93.8\%, and F-scores of 0.99, 0.82, and 0.95, for healthy, mild and moderate/severe cases, respectively.

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