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dc.contributor.author | Fernández Fuentes, Xosé |
dc.contributor.author | Mera Pérez, David |
dc.contributor.author | Gómez, Andrés |
dc.contributor.author | Vidal-Franco, Ignacio |
dc.date.accessioned | 2019-10-24T10:27:25Z |
dc.date.available | 2019-10-24T10:27:25Z |
dc.date.issued | 2018 |
dc.identifier.citation | 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 |
dc.identifier.uri | http://hdl.handle.net/10347/19924 |
dc.description.abstract | 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 |
dc.description.sponsorship | 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) |
dc.language.iso | eng |
dc.publisher | MDPI |
dc.rights | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.subject | Electrical impedance tomography |
dc.subject | Machine learning |
dc.subject | Artificial neural networks |
dc.subject | Inverse problems |
dc.subject | Conductivity |
dc.title | Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach |
dc.type | journal article |
dc.identifier.doi | 10.3390/electronics7120422 |
dc.relation.publisherversion | https://doi.org/10.3390/electronics7120422 |
dc.type.hasVersion | VoR |
dc.identifier.essn | 2079-9292 |
dc.rights.accessRights | open access |
dc.contributor.affiliation | Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información |
dc.description.peerreviewed | SI |
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