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Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet

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Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1229))

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Abstract

Skin hydration measurement is very important for many clinical studies. Skin capacitive imaging is a novel technique that can be used for in-vivo skin hydration measurements. It is based on permittivity measurement principle, and can generate a skin water content image using a capacitive matrix sensor. In this paper, we present our latest study on the skin capacitive imaging analysis using Deep Learning GoogLeNet. We also present the development of a graphical user interface programme in order to simply the usage and improve the efficiency. The skin capacitive images are divided into three groups according to volunteers (V1, V2, V3), gender (male and female), and skin sites (face, forearm, forehead, neck, palm, and lower leg). GoogLeNet is used for image classifications. The results show that GoogLeNet can effectively differentiate the different skin capacitive images from different categories. We will first present the skin capacitive imaging technology and then present the experimental results.

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Correspondence to Perry Xiao .

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Zhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D., Xiao, P. (2020). Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_29

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