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A Fuzzy Logic Technique for Optimizing Follicular Units Measurement of Hair Transplantation

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Recent Advances on Soft Computing and Data Mining (SCDM 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 978))

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Abstract

Hair transplantation medical procedure is one of the main methods that are at present utilized in the treatment of balding of the scalp. It is essentially a procedure of extricating or taking a particular number of Follicular Units (FUs) from the back of the head which serves as the contributor or donor region and transplanting them in the region of the scalp that is going bald. A FU comprises one to five normally occurring human skin hairs. The most mainstream techniques designed for hair transplantation dependent on the FUs idea is the Follicular Units Extraction (FUE). Past endeavors to calculate the needed number of FUs for the FUE failed to put into consideration various metrics or indices (parameters) associated with the determination procedure. This paper expounds a Fuzzy Logic Follicular Units Measurement (FL-FUM) strategy for hair transplantation of the FUT and FUE techniques. The FL-FUM technique gives a progressively exact estimation of the needed FUs number by envisaging about three fuzzy metrics of Age, Race and Donor Area Density (DAD). Its objective is to help hair reclamation people who utilize the FUT and FUE techniques in assessing the needed number of necessary grafts that fulfill a patient’s baldness state. The FL-FUM strategy employs a Fuzzy Logic system on the three metrics (fuzzy sets) to defuzzify the assessment of the FUs dependent on Visualized Male Pattern Baldness Schema. The strategy is tried and assessed by contrasting its outcomes and the comparable existing strategies and is observed to be highly productive for real estimation cases.

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Acknowledgements

This work is sponsored by Universiti Tun Hussein Onn Malaysia under TIER1 FASA 1/2007, UTHM Research Grant (VOT U896) and Gates IT Sdn. Bhd.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Salama A. Mostafa, Azizul Azhar Ramli, Aida Mustapha & Rabei Raad Ali

  2. College of Computing and Informatics, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia

    Abdullah S. Alsobiae

Authors
  1. Salama A. Mostafa
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  2. Abdullah S. Alsobiae
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  3. Azizul Azhar Ramli
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  4. Aida Mustapha
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  5. Rabei Raad Ali
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Corresponding author

Correspondence to Salama A. Mostafa .

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Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  4. School of Information Technology, Deakin University, Geelong Waurn Ponds Campus, VIC, Australia

    Jemal H. Abawajy

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Mostafa, S.A., Alsobiae, A.S., Ramli, A.A., Mustapha, A., Ali, R.R. (2020). A Fuzzy Logic Technique for Optimizing Follicular Units Measurement of Hair Transplantation. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_30

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