Electrical Impedance Tomography for Anisotropic Media: a Machine Learning Approach to Classify Inclusions
This work addresses the inverse inclusion problem in medical or material imaging by integrating machine learning with classical EIT analysis, though it appears incremental as it applies existing methods to a specific domain.
The paper tackles the problem of identifying inclusions in anisotropic media using Electrical Impedance Tomography by applying machine learning techniques like Artificial Neural Networks and Support Vector Machines to classify inclusions based on boundary measurements, achieving high detection rates and showing that two measurements suffice for accurate size prediction.
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.