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Detalhes Referência

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

Título
Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue

Participantes na publicação
Branislav Gerazov (Author)
Daphne Anne Caligari Conti (Author)
Laura Farina (Author)
Lourdes Farrugia (Author)
Charles V. Sammut (Author)
Pierre Schembri Wismayer (Author)
Raquel C. Conceição (Author)
Dep. Física
 IBEB
 IBEB
Resumo
In this paper we revisited a database with measurements of the dielectric properties of rat muscles. Measurements were performed both in vivo and ex vivo; the latter were performed in tissues with varying levels of hydration. Dielectric property measurements were performed with an open-ended coaxial probe between the frequencies of 500 MHz and 50 GHz at a room temperature of 25 °C. In vivo dielectric properties are more valuable for creating realistic electromagnetic models of biological tissue, but these are more difficult to measure and scarcer in the literature. In this paper, we used machine learning models to predict the in vivo dielectric properties of rat muscle from ex vivo dielectric property measurements for varying levels of hydration. We observed promising results that suggest that our model can make a fair estimation of in vivo properties from ex vivo properties.

Data de Submissão/Pedido
2021-09-24
Data de Aceitação
2021-10-16
Data de Publicação
2021-10-19

Suporte
Sensors

Identificadores da Publicação
ISSN - 1424-8220
eISSN - 1424-8220

Editora
MDPI

Coleção
Advances in Medical Microwave Imaging and Signal Processing, and Hyperthermic Technologies for Healthcare

Volume
21
Fascículo
20

Página Inicial
6935

Identificadores do Documento
DOI - https://doi.org/10.3390/s21206935

Identificadores de Qualidade
SCIMAGO Q1 (2016) - 0.623 - Electrical and Electronic Engineering
SCOPUS Q1 (2016) - 0.623 - Electrical and Electronic Engineering
SCOPUS Q1 (2017) - 0.584 - Instrumentation
SCOPUS Q1 (2017) - 0.584 - Atomic and Molecular Physics, and Optics
SCIMAGO Q1 (2019) - 0.653 - Instrumentation
SCIMAGO Q1 (2019) - 0.653 - Atomic and Molecular Physics, and Optics (Q2)
SCIMAGO Q1 (2019) - 0.653 - Electrical and Electronic Engineering (Q2)
SCIMAGO Q1 (2019) - 0.653 - Information Systems (Q2)
SCOPUS Q1 (2019) - 5 - Instrumentation
SCOPUS Q1 (2019) - 5 - Electrical and Electronic Engineering
SCOPUS Q1 (2019) - 5 - Electrical and Electronic Engineering
Web Of Science Q1 (2020) - 3.576 - INSTRUMENTS & INSTRUMENTATION - SCIE


Exportar referência

APA
Branislav Gerazov, Daphne Anne Caligari Conti, Laura Farina, Lourdes Farrugia, Charles V. Sammut, Pierre Schembri Wismayer, Raquel C. Conceição, (2021). Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue. Sensors, 21, ISSN 1424-8220. eISSN 1424-8220.

IEEE
Branislav Gerazov, Daphne Anne Caligari Conti, Laura Farina, Lourdes Farrugia, Charles V. Sammut, Pierre Schembri Wismayer, Raquel C. Conceição, "Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue" in Sensors, vol. 21, 2021. 10.3390/s21206935

BIBTEX
@article{52469, author = {Branislav Gerazov and Daphne Anne Caligari Conti and Laura Farina and Lourdes Farrugia and Charles V. Sammut and Pierre Schembri Wismayer and Raquel C. Conceição}, title = {Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue}, journal = {Sensors}, year = 2021, volume = 21 }