RT Journal Article
T1 Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach
A1 Fernández Fuentes, Xosé
A1 Mera Pérez, David
A1 Gómez, Andrés
A1 Vidal-Franco, Ignacio
K1 Electrical impedance tomography
K1 Machine learning
K1 Artificial neural networks
K1 Inverse problems
K1 Conductivity
AB Different industrial and medical situations require the non-invasive extraction of information from the inside of bodies. This is usually done through tomographic methods that generate images based on internal body properties. However, the image reconstruction involves a mathematical inverse problem, for which accurate resolution demands large computation time and capacity. In this paper we explore the use of Machine Learning to develop an accurate solver for reconstructing Electrical Impedance Tomography images in real-time. We compare the results with the Iterative Gauss-Newton and the Primal Dual Interior Point Method, which are both largely used and well-validated solvers. The approaches were compared from the qualitative as well as the quantitative viewpoints. The former was focused on correctly detecting the internal body features. The latter was based on accurately predicting internal property distributions. Experiments revealed that our approach achieved better accuracy and Cohen’s kappa coefficient (97.57% and 94.60% respectively) from the qualitative viewpoint. Moreover, it also obtained better quantitative metrics with a Mean Absolute Percentage Error of 18.28%. Experiments confirmed that Neural Networks algorithms can reconstruct internal body properties with high accuracy, so they would be able to replace more complex and slower alternatives
PB MDPI
YR 2018
FD 2018
LK http://hdl.handle.net/10347/19924
UL http://hdl.handle.net/10347/19924
LA eng
NO Fernández-Fuentes, X., Mera, D., Gómez, A., & Vidal-Franco, I. (2018). Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach. Electronics, 7(12), 422. doi: 10.3390/electronics7120422
NO This work has received financial support from the predoctoral scholarship program of the Xunta de Galicia (ED481A-2018/277), the Xunta de Galicia under Research Network R2016/045, the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08) and the European Regional Development Fund (ERDF)
DS Minerva
RD 24 abr 2026