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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References
Fluhr, J.W., Elsner, P., Berardesca, E., Maibach, H.I. (eds.): Bioengineering of the Skin: Water and the Stratum Corneum, 2nd edn. Dermatology: Clinical & Basic Science Book, vol. 23. CRC Press (2004)
Xiao, P.: Optothermal measurement of water distribution within stratum corneum. In: Humbert, P., Fanian, F., Maibach, H.I., Agache, P. (eds.) Agache’s Measuring the Skin, 2nd edn. p. 355, Chapter 31. Springer (2017). ISBN 978-3-319-32381-7
Singh, H., Xiao, P., Berg, E.P., Imhof, R.E.: In-vivo skin imaging for hydration and micro relief measurements. In: Stratum Corneum V, Cardiff, UK, 11–13 July (2007)
Imhof, R.E., Xiao, P.: Biox epsilon - a new permittivity imaging system. In: The ISBS World Congress, Copenhagen, Denmark, 28–30 November (2012)
Ou, X., Pan, W., Xiao, P.: In-vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). Int. J. Pharm. 460(1–2), 28–32 (2014)
Xiao, P., Bontozoglou, C.: Capacitive contact imaging for in-vivo hair and nail water content measurements. H&PC Today 10(5), 62–65 (2015)
Bontozoglou, C., Zhang, X., Xiao, P.: Micro-relief analysis with skin capacitive imaging. Skin Res. Technol. 25(2), 165–170 (2019)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)
Lu, S., Wang, B., Wang, H., Chen, L., Ma, L., Zhang, X.: A real-time object detection algorithm for video. Comput. Electr. Eng. 77, 398–408 (2019)
Zhou, T., Han, G., Li, B., Lin, Z., Ciaccio, E.J., Green, P.H., Qin, J.: Quantitative analysis of patients with celiac disease by video capsule endoscopy: a deep learning method. Comput. Biol. Med. 85, 1–6 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR 2015 arXiv:1409.1556v6 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. arXiv:1512.03385 (2015)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size arXiv:1602.07360v4 (2016)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. arXiv:1608.06993v5 (2016)
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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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DOI: https://doi.org/10.1007/978-3-030-52246-9_29
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