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Multi-objective quantum tunicate swarm optimization with deep learning model for intelligent dystrophinopathies diagnosis

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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.

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Data sharing not applicable to this article as no datasets were generated during the current study.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/209/42). www.kku.edu.sa.

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Authors and Affiliations

  1. Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia

    Fahd N. Al-Wesabi

  2. Faculty of Computer and IT, Sana’a University, Sana’a, Yemen

    Fahd N. Al-Wesabi

  3. Department of electronics and communications Engineering, Mansoura University, Mansoura, Egypt

    Marwa Obayya

  4. Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia

    Anwer Mustafa Hilal

  5. Tijuana Institute Technology, Tijuana, Mexico

    Oscar Castillo

  6. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, Delhi, India

    Deepak Gupta & Ashish Khanna

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  1. Fahd N. Al-Wesabi
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  2. Marwa Obayya
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  3. Anwer Mustafa Hilal
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  4. Oscar Castillo
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  5. Deepak Gupta
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  6. Ashish Khanna
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Correspondence to Fahd N. Al-Wesabi.

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The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Communicated by Oscar Castillo.

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Al-Wesabi, F.N., Obayya, M., Hilal, A.M. et al. Multi-objective quantum tunicate swarm optimization with deep learning model for intelligent dystrophinopathies diagnosis. Soft Comput 27, 13077–13092 (2023). https://doi.org/10.1007/s00500-021-06620-5

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  • DOI: https://doi.org/10.1007/s00500-021-06620-5

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