Electrical impedance tomography for anisotropic media: A machine learning approach to classify inclusions

• Machine learning techniques can effectively classify the Dirichlet-to-Neumann matrix in Electrical Impedance Tomography (EIT). • The presence of a circular inclusion can be identified with high accuracy via Support Vector Machines methods (SVMs). • The radius of a circular inclusion can be determined with high accuracy via Artificial Neural Networks (ANNs). • Special types of anisotropic inclusions can be identified with high accuracy via ANNs. We consider the problem in Electrical Impedance Tomography (EIT) of identifying one or multiple inclusions in a background-conducting body Ω ⊂ R 2 , from the knowledge of a finite number of electrostatic measurements taken on its boundary ∂Ω 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.