S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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An Expert Smart Scalp Inspection System Using Deep Learning

Sin-Ye Jhong, Po-Yen Yang, and Chih-Hsien Hsia

(Received June 15, 2021; Accepted September 15, 2021)

Keywords: smart scalp inspection, beauty economy, embedded system, deep learning

With the advent of the “beauty economic era,” in which people are paying more attention to beauty and health, the health of the scalp is being increasingly valued. However, current scalp care services are limited by problems such as they are not automatic and objective, and the results are not significant, which make them unacceptable to the public. Because of these reasons, in this study, we focus on the obstacles that hairdressers face and propose an expert inspection system that is suitable for determining scalp problems by utilizing deep learning, cloud computing techniques, and an embedded system. Dandruff is the most common scalp problem. In this work, we propose a convolutional neural network (CNN)-based method to analyze the severity of dandruff and evaluate the health of the scalp. The convolutional block attention module (CBAM) is adopted to improve the feature extraction performance of the CNN model. The depth separable convolution (DSC) and spinal fully connected (FC) are applied in this work to reduce the number of model parameters. Aside from offering a more effective smart scalp inspection process, this method also lets hairdressers and customers track their scalp problems easily. In the future, we expect to reduce the stress of hairdressers and enhance customers’ trust on scalp care services by using the smart health inspection offered by this system. Last but not least, it has been shown that the method proposed in this research can achieve an accuracy of 85.03%, which is higher than that achieved by recently proposed methods.

Corresponding author: Chih-Hsien Hsia




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