Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet

Conference paper


Zhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D. and Xiao, P. (2019). Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet. Computing Conference 2020. London, UK 16 - 17 Jul 2019 Springer.
AuthorsZhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D. and Xiao, P.
TypeConference paper
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 [1-3]. It is based on permittivity measurement principle, and can generate a skin water content image using a matrix sensor. In this paper, we present our latest study on the skin capacitive imaging analysis using Deep Learning GoogLeNet [4]. The skin capacitive images are divided into three groups according to volunteers, 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.

This is a post-peer-review, pre-copyedit version of an article published in Advances in Intelligent Systems and Computing.

Year2019
JournalAdvances in Intelligent Systems and Computing
PublisherSpringer
ISSN 2194-5357
Accepted author manuscript
License
CC BY 4.0
File Access Level
Open
Publication dates
Print16 Jul 2020
Publication process dates
Accepted26 Nov 2019
Deposited16 Dec 2019
Permalink -

https://openresearch.lsbu.ac.uk/item/88qz3

Accepted author manuscript

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