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Advisor(s)
Abstract(s)
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 were evaluated on axial T1-weighted Turbo Spin-Echo
MRI from 26 subjects, including Ullrich Congenital Muscular Dystrophy and
Bethlem Myopathy patients at different evolution stages. The hybrid model
achieved the best cross-validation results, with 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.
Description
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Keywords
Collagen VI-related myopathy MRI Computer-aided diagnosis Texture analysis Convolutional Neural Networks