Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/59629
Title: Application of artificial neural networks for accurate determination of the complex permittivity of biological tissue
Authors: Bonello, Julian
Demarco, Andrea
Farhat, Iman
Farrugia, Lourdes
Sammut, Charles V.
Keywords: Neural networks (Computer science)
Dielectrics
Tissues -- Analysis
Radio frequency
Microwave imaging in medicine
Issue Date: 2020
Publisher: MDPI
Citation: Bonello, J., Demarco, A., Farhat, I., Farrugia, L., & Sammut. C. V. (2020). Application of artificial neural networks for accurate determination of the complex permittivity of biological tissue. Sensors, 20, 4640.
Abstract: Medical devices making use of radio frequency (RF) and microwave (MW) fields have been studied as alternatives to existing diagnostic and therapeutic modalities since they offer several advantages. However, the lack of accurate knowledge of the complex permittivity of different biological tissues continues to hinder progress in of these technologies. The most convenient and popular measurement method used to determine the complex permittivity of biological tissues is the open-ended coaxial line, in combination with a vector network analyser (VNA) to measure the reflection coefficient (S11) which is then converted to the corresponding tissue permittivity using either full-wave analysis or through the use of equivalent circuit models. This paper proposes an innovative method of using artificial neural networks (ANN) to convert measured S11 to tissue permittivity, circumventing the requirement of extending the VNA measurement plane to the coaxial line open end. The conventional three-step calibration technique used with coaxial open-ended probes lacks repeatability, unless applied with extreme care by experienced persons, and is not adaptable to alternative sensor antenna configurations necessitated by many potential diagnostic and monitoring applications. The method being proposed does not require calibration at the tip of the probe, thus simplifying the measurement procedure while allowing arbitrary sensor design, and was experimentally validated using S11 measurements and the corresponding complex permittivity of 60 standard liquid and 42 porcine tissue samples. Following ANN training, validation and testing, we obtained a prediction accuracy of 5% for the complex permittivity.
URI: https://www.um.edu.mt/library/oar/handle/123456789/59629
Appears in Collections:Scholarly Works - FacSciPhy

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