# Microwave Tomography with phaseless data on the calcaneus by means of artificial neural networks

## TL;DR

This study employs a multilayer perceptron neural network to perform phaseless microwave imaging of the human heel, accurately estimating size and location despite noise, using simulated 2D dielectric maps inspired by CT images.

## Contribution

It introduces a neural network-based approach for phaseless microwave tomography of the calcaneus, addressing shape influence and noise robustness in 2D simulations.

## Key findings

- Accurate size and location estimation with ~3 mm error.
- Better dielectric property estimation for surrounding tissue (~15% error).
- Robustness of the method under noisy conditions demonstrated.

## Abstract

The aim of this study is to use a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) for phaseless imaging the human heel (modeled as a bilayer dielectric media: bone and surrounding tissue) and the calcaneus cross-section size and location using a two dimensional (2D) microwave tomographic array. Computer simulations were performed over 2D dielectric maps inspired by Computed Tomography (CT) images of human heels for training and testing the MLP. A morphometric analysis was performed to account for the scatterer shape influence on the results. A robustness analysis was also conducted in order to study the MLP performance in noisy conditions. The standard deviations of the relative percentage errors on estimating the dielectric properties of the calcaneus bone were relatively high. Regarding the calcaneus surrounding tissue, the dielectric parameters estimations are better, with relative percentage error standard deviations up to $\approx$ 15 %. The location and size of the calcaneus are always properly estimated with absolute error standard deviations up to $\approx $ 3 mm.