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Authors: Christoforos Galazis 1 ; Sergey Vesnin 2 and Igor Goryanin 3

Affiliations: 1 University of Edinburgh, Edinburgh, U.K. ; 2 Medical Microwave Radiometry Ltd., U.K. ; 3 University of Edinburgh, Edinburgh, U.K., Okinawa Institute of Science and Technology, Okinawa, Japan

ISBN: 978-989-758-353-7

Keyword(s): Microwave Radiometry, Breast Cancer, Diagnostic System, Artificial Intelligence, Machine Learning, Neural Network, Cascade Correlation Neural Network, Convolutional Neural Network, Random Forest, Support Vector Machine.

Abstract: Microwave radiometry is being developed more actively in recent years for medical applications. One such application is for diagnosis or monitoring of cancer. Medical radiometry presents a strong alternative to other methods of diagnosis, especially with recent gains in its accuracy. In addition, it is safe to use, noninvasive and has a relative low cost of use. Temperature readings were taking from the mammary glands for the purpose of detecting cancer and evaluating the effectiveness of radiometry. Building a diagnostic system to automate classification of new samples requires an adequate machine learning model. Such models that were explored were random forest, XGBoost, k-nearest neighbors, support vector machines, variants of cascade correlation neural network, deep neural network and convolution neural network. From all these models evaluated, the best performing on the test set was the deep neural network with a significant difference from the rest.

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Paper citation in several formats:
Galazis, C.; Vesnin, S. and Goryanin, I. (2019). Application of Artificial Intelligence in Microwave Radiometry (MWR).In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-353-7, pages 112-122. DOI: 10.5220/0007567901120122

@conference{bioinformatics19,
author={Christoforos Galazis. and Sergey Vesnin. and Igor Goryanin.},
title={Application of Artificial Intelligence in Microwave Radiometry (MWR)},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2019},
pages={112-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007567901120122},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Application of Artificial Intelligence in Microwave Radiometry (MWR)
SN - 978-989-758-353-7
AU - Galazis, C.
AU - Vesnin, S.
AU - Goryanin, I.
PY - 2019
SP - 112
EP - 122
DO - 10.5220/0007567901120122

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