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

TL;DR

This study develops and evaluates three machine learning methods, including a hybrid CNN and texture feature approach, for classifying muscle involvement severity in Collagen VI-related myopathy using MRI data, achieving high accuracy.

Contribution

The paper introduces a hybrid classification method combining CNN and handcrafted texture features for improved severity assessment in neuromuscular MRI analysis.

Findings

Hybrid model achieved 93.8% accuracy.

F-scores of 0.99, 0.82, and 0.95 for different severity levels.

Proposed methods outperform individual models.

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 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.