Electrical Impedance Tomography for Anisotropic Media: a Machine Learning Approach to Classify Inclusions
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916601098403840 |
|---|---|
| author | Gaburro, Romina Healy, Patrick Naidu, Shraddha Nolan, Clifford |
| author_facet | Gaburro, Romina Healy, Patrick Naidu, Shraddha Nolan, Clifford |
| contents | We consider the problem in Electrical Impedance Tomography (EIT) of identifying one or multiple inclusions in a background-conducting body $Ω\subset\mathbb{R}^2$, from the knowledge of a finite number of electrostatic measurements taken on its boundary $\partialΩ$ and modelled by the Dirichlet-to-Neumann (D-N) matrix. Once the presence of one inclusion in $Ω$ is established, our model, combined with the machine learning techniques of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), may be used to determine the size of the inclusion, the presence of multiple inclusions, and also that of anisotropy within the inclusion(s). Utilising both real and simulated datasets within a 16-electrode setup, we achieve a high rate of inclusion detection and show that two measurements are sufficient to achieve a good level of accuracy when predicting the size of an inclusion. This underscores the substantial potential of integrating machine learning approaches with the more classical analysis of EIT and the inverse inclusion problem to extract critical insights, such as the presence of anisotropy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Electrical Impedance Tomography for Anisotropic Media: a Machine Learning Approach to Classify Inclusions Gaburro, Romina Healy, Patrick Naidu, Shraddha Nolan, Clifford Numerical Analysis Machine Learning 65N21, 35R30, 68T99 We consider the problem in Electrical Impedance Tomography (EIT) of identifying one or multiple inclusions in a background-conducting body $Ω\subset\mathbb{R}^2$, from the knowledge of a finite number of electrostatic measurements taken on its boundary $\partialΩ$ and modelled by the Dirichlet-to-Neumann (D-N) matrix. Once the presence of one inclusion in $Ω$ is established, our model, combined with the machine learning techniques of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), may be used to determine the size of the inclusion, the presence of multiple inclusions, and also that of anisotropy within the inclusion(s). Utilising both real and simulated datasets within a 16-electrode setup, we achieve a high rate of inclusion detection and show that two measurements are sufficient to achieve a good level of accuracy when predicting the size of an inclusion. This underscores the substantial potential of integrating machine learning approaches with the more classical analysis of EIT and the inverse inclusion problem to extract critical insights, such as the presence of anisotropy. |
| title | Electrical Impedance Tomography for Anisotropic Media: a Machine Learning Approach to Classify Inclusions |
| topic | Numerical Analysis Machine Learning 65N21, 35R30, 68T99 |
| url | https://arxiv.org/abs/2502.04273 |