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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

T. Gopalakrishnan1, Periakaruppan Sudhakaran2, K. C. Ramya3, K. Sathesh Kumar4, Fahd N. Al-Wesabi5,6,*, Manal Abdullah Alohali7, Anwer Mustafa Hilal8

1 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India
2 Department of Computer Science and Engineering, SRM TRP Engineering College, Tiruchirappalli, 621105, India
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
4 School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India
5 Department of Computer Science, King Khalid University, Muhayel Aseer, KSA
6 Faculty of Computer and IT, Sana'a University, Yemen
7 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia
8 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia

* Corresponding Author: Fahd N. Al-Wesabi. Email: email

Computers, Materials & Continua 2022, 71(1), 305-320.


Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such as muscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging (MRI). Among these techniques, Muscle MRI recommends the diagnosis of muscular dystrophy through identification of the patterns that exist in muscle fatty replacement. But the patterns overlap among various diseases whereas there is a lack of knowledge prevalent with regards to disease-specific patterns. Therefore, artificial intelligence techniques can be used in the diagnosis of muscular dystrophies, which enables us to analyze, learn, and predict for the future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using Synergic Deep Learning (SDL) method with extreme Gradient Boosting (XGBoost), called SDL-XGBoost. SDL-XGBoost model has been proposed to act as an automated deep learning (DL) model that examines the muscle MRI data and diagnose muscular dystrophies. SDL-XGBoost model employs Kapur's entropy based Region of Interest (RoI) for detection purposes. Besides, SDL-based feature extraction process is applied to derive a useful set of feature vectors. Finally, XGBoost model is employed as a classification approach to determine proper class labels for muscle MRI data. The researcher conducted extensive set of simulations to showcase the superior performance of SDL-XGBoost model. The obtained experimental values highlighted the supremacy of SDL-XGBoost model over other methods in terms of high accuracy being 96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacement in muscle MRI.


Cite This Article

T. Gopalakrishnan, P. Sudhakaran, K. C. Ramya, K. Sathesh Kumar, F. N. Al-Wesabi et al., "An automated deep learning based muscular dystrophy detection and classification model," Computers, Materials & Continua, vol. 71, no.1, pp. 305–320, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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