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Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

by 1,†, 1,† and 2,*
1
Department of Computer Science, Graduate School, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si 16227, Gyeonggi-do, Korea
2
Division of AI & Computer Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si 16227, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Nikolaos Doulamis
Sensors 2022, 22(2), 650; https://doi.org/10.3390/s22020650 (registering DOI)
Received: 6 December 2021 / Revised: 7 January 2022 / Accepted: 13 January 2022 / Published: 14 January 2022
(This article belongs to the Section Intelligent Sensors)
Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67. View Full-Text
Keywords: deep learning; follicle detection; hair density measurement; hair transplant; object detection deep learning; follicle detection; hair density measurement; hair transplant; object detection
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MDPI and ACS Style

Kim, M.; Kang, S.; Lee, B.-D. Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks. Sensors 2022, 22, 650. https://doi.org/10.3390/s22020650

AMA Style

Kim M, Kang S, Lee B-D. Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks. Sensors. 2022; 22(2):650. https://doi.org/10.3390/s22020650

Chicago/Turabian Style

Kim, Minki, Sunwon Kang, and Byoung-Dai Lee. 2022. "Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks" Sensors 22, no. 2: 650. https://doi.org/10.3390/s22020650

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