Applying the logistic regression in electrical impedance tomography to analyze conductivity of the examined objects

Issue title: Selected papers from the International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering – ISEF 2019

Guest editors: Paolo Di Barba, Maria Evelina Mognaschi and Sławomir Wiak

Article type: Research Article

Authors: Rymarczyk, Tomasz^{a; b;} | Kozłowski, Edward^c | Tchórzewski, Paweł^b | Kłosowski, Grzegorz^c | Adamkiewicz, Przemysław^{a; b}

Affiliations: [a] University of Economics and Innovation in Lublin, Lublin, Poland | [b] Research and Development Centre Netrix S.A., Lublin, Poland | [c] Lublin University of Technology, Lublin, Poland

Correspondence: [*] Corresponding author: Tomasz Rymarczyk, University of Economics and Innovation in Lublin, Projektowa 4, 20-209 Lublin, Poland. E-mail: [email protected]

Abstract: The article presents machine learning methods in the field of reconstruction of tomographic images. The presented research results show that electric tomography makes it possible to analyze objects without interfering with them. The work focused mainly on electrical impedance tomography and image reconstruction using deterministic methods and machine learning, reconstruction results were compared and various numerical models were used. The main advantage of the presented solution is the ability to analyze spatial data and high speed of processing. The implemented algorithm based on logistic regression is promising in image reconstruction. In addition, the elastic net method was used to solve the problem of selecting input variables in the regression model.

Keywords: Electrical tomography, inverse problem, logistic regression

DOI: 10.3233/JAE-209520

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 64, no. S1, pp. S235-S252, 2020