Neural Network Approach for Dielectric Characterization of Tissues in Microwave Frequencies using Coplanar Waveguide Transmission

This paper presents an extension to previous work, using neural networks to characterize materials in microwave frequencies, to extend the applicability of a deep learning model to be able to characterize the dielectric properties of biological tissues. A neural network model using convolutional and fully connected layers is designed to predict the permittivity and loss tangent using the scattering parameters from a coplanar waveguide transmission sensor. Simulated data from the sensor provide a large dataset, with a wide range of values for the permittivity and loss tangent, which is used to train and test the model. The trained network is validated by predicting the output parameters on the test set. Compared with previous work, by using convolutional layers the applicable parameter space is vastly extended while keeping satisfying levels of accuracy. A complete system with a trained network is proposed to be used in a lab or in clinics.

Recommended citation: Viktor Mattsson, et al. (2020). "Neural Network Approach for Dielectric Characterization of Tissues in Microwave Frequencies using Coplanar Waveguide Transmission." 2020 14th European Conference on Antennas and Propagation (EuCAP). https://ieeexplore.ieee.org/abstract/document/9135949