Abstract
Dystrophinopathies are commonly affecting inherited muscular disease over the globe. Magnetic resonance imaging (MRI) is widely employed as a significant tool to diagnose dystrophinopathies. Though MRI is effective, it is mainly based on personal experiences and can simply result in misdiagnosis. This study designs a multi-objective quantum tunicate swarm optimization with deep learning (MOQTSO-DL) model to diagnose dystrophinopathies using muscle MRI images. The proposed model involves a RoI detection process by an optimized region growing approach where the initial seed points and thresholds are effectively determined by the MOQTSO algorithm. Besides, capsule network (CapsNet) is employed as a feature extractor to derive an optimal set of features. Moreover, MOQTSO with extreme learning machine (ELM) based classifier is used to allocate appropriate class labels for the muscle MRI images. The design of the MOQTSO algorithm for the initial seed point selection of RoI detection and parameter tuning of the ELM model depicts the novelty of the work. Extensive experimental analysis is carried out to showcase the improved performance of the proposed method. The simulation outcomes reported the better classification outcomes of the MOQTSO-DL method over the other compared methods.