RT Journal Article
T1 Study and comparison of different Machine Learning-based approaches to solve the inverse problem in Electrical Impedance Tomographies
A1 Aller Domínguez, Martín
A1 Mera Pérez, David
A1 Cotos Yáñez, José Manuel
A1 Villarroya Fernández, Sebastián
K1 Machine learning
K1 Artificial neural networks
K1 Gradient boosting
K1 Electrical impedance tomography
AB Electrical Impedance Tomography (EIT) is a non-invasive technique used to obtain the electrical internal conductivity distribution from the interior of bodies. This is a promising method from the manufacturing viewpoint, since it could be used to estimate different physical inner body properties during the production of goods. Nevertheless, this technique requires dealing with an inverse problem that makes its usage in real-time processes challenging. Recently, Machine Learning techniques have been proposed to solve the inverse problem accurately. However, the majority of prior research is focused on qualitative results, and they typically lack a systematic methodology to determine the optimal hyperparameters appropriately. This work presents a systematic comparison of six popular Machine Learning algorithms: Artificial Neural Network, Random Forest, K-Nearest Neighbors, Elastic Net, Ada Boost, and Gradient Boosting. Particularly, the last two algorithms were based on decision tree learners. Furthermore, we studied the relationship between model performance and different EIT configurations. Specifically, we analyzed whether the measurement pattern and the number of used electrodes could increase the model performance. Experiments revealed that tree-based models present high performance, even better than Neural Networks, the most widely-used Machine Learning model to deal with EIT. Experiments also showed a model performance improvement when the EIT configuration was optimized. Most favorable metrics were attained using the tree-based Gradient Boosting model with a combination of both adjacent and mono measurement patterns as well as with 32 electrodes deployed during the tomographic process. With this particular setting, we achieved an accuracy of 99.14% detecting internal artifacts and a Root Mean Square Error of 4.75 predicting internal conductivity distributions
PB Springer
SN 0941-0643
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29992
UL http://hdl.handle.net/10347/29992
LA eng
NO Aller, M., Mera, D., Cotos, J.M. et al. Study and comparison of different Machine Learning-based approaches to solve the inverse problem in Electrical Impedance Tomographies. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-022-07988-7
NO This work has received financial support from the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019–2022 ED431G-2019/04) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela as a Research Center of the Galician University System. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
DS Minerva
RD 24 abr 2026