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Developing Charcot–Marie–Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based Spiking Neural Network

  • Patient Facing Systems
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Abstract

In the developing technology Charcot–Marie–Tooth (CMT) disease is one of the teeth diseases which are occurred due to the genetic reason. The CMT disease affects the muscle tissue which reduces the progressive growth of the muscle. So, the CMT disease needs to be recognized carefully for eliminating the risk factors in the early stage. At the time of this process, the system handles the difficulties while performing feature extraction and classification part. So, the teeth images are processed by applying the normalization method which eliminates the salt and pepper noise from data. From that, modified group delay function along with Cepstral coefficient features are extracted with effective manner. After that Bacterial Foraging Optimization Algorithm based features are selected. Then the selected features are examined by applying the Bacterial Foraging Optimization Algorithm based spiking neural network which successfully recognizes the CMT disease. At that point the productivity of the framework is assessed with the assistance of exploratory outcomes.

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Acknowledgements

The project was financially supported by Vice Deanship of Research Chairs, King Saud University, Riyadh, Kingdom of Saudi Arabia.

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Authors and Affiliations

  1. Dental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, P.O Box 10219, Riyadh, 11433, Saudi Arabia

    Abdulaziz Abdullah Al-Kheraif & Mohamed Hashem

  2. College of Engineering, King Saud University, Riyadh, Saudi Arabia

    Mohammed Sayed S. Al Esawy

Authors
  1. Abdulaziz Abdullah Al-Kheraif
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  2. Mohamed Hashem
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  3. Mohammed Sayed S. Al Esawy
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Corresponding author

Correspondence to Abdulaziz Abdullah Al-Kheraif.

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The authors declare no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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This article is part of the Topical Collection on Patient Facing Systems

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Al-Kheraif, A.A., Hashem, M. & Al Esawy, M.S.S. Developing Charcot–Marie–Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based Spiking Neural Network. J Med Syst 42, 192 (2018). https://doi.org/10.1007/s10916-018-1049-8

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  • DOI: https://doi.org/10.1007/s10916-018-1049-8

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