In this paper, a microwave imaging approach based on artificial neural networks (ANNs) for neck tumor detection is presented. The aim of this technique is to retrieve the geometric and dielectric properties of the neck to identify the possible presence of tumors, starting from scattered electric field data. A fully-connected neural network is developed to test the feasibility of the proposed approach. Moreover, a numerical model including the main features of a cross section of the neck is specifically designed in order to create a suitable training dataset. Subsequently, for the optimization of the ANN architecture and performance evaluation, a numerical analysis is conducted. A set of simulated cases, based on realistic neck phantoms, is tested to evaluate the robustness of the network. Preliminary results show the possibility to identify and locate neck tumors.

Microwave imaging of the neck by means of artificial neural networks for tumor detection

Dachena, Chiara;Fedeli, Alessandro;Randazzo, Andrea;Pastorino, Matteo
2021-01-01

Abstract

In this paper, a microwave imaging approach based on artificial neural networks (ANNs) for neck tumor detection is presented. The aim of this technique is to retrieve the geometric and dielectric properties of the neck to identify the possible presence of tumors, starting from scattered electric field data. A fully-connected neural network is developed to test the feasibility of the proposed approach. Moreover, a numerical model including the main features of a cross section of the neck is specifically designed in order to create a suitable training dataset. Subsequently, for the optimization of the ANN architecture and performance evaluation, a numerical analysis is conducted. A set of simulated cases, based on realistic neck phantoms, is tested to evaluate the robustness of the network. Preliminary results show the possibility to identify and locate neck tumors.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1061027
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