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Application of Data Mining and Machine Learning in Microwave Radiometry (MWR)

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Biomedical Engineering Systems and Technologies (BIOSTEC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1211))

Abstract

Microwave radiometry has seen its way in successful usage in medical applications. The focus here is its applicability in cancer detection and monitoring, specifically for breast cancer, as an additional and alternative tool. This is done by capturing the temperature of the skin and the internal tissue. However, the amount of data required by clinical specialist to process in a short time to reach to a confident decision is becoming insurmountable. This can be tackled by developing a diagnostic system that will help pinpoint irregularities associated with pathologies. The key factors of a successful diagnostic system is the accuracy of the predictions and its informativeness and interpretability. The core component of such a system is a machine learning algorithm. Models that were explored were random forest, k-nearest neighbors, support vector machines, variants of cascade correlation neural networks, deep neural network and convolution neural network. From all these models evaluated, the best performing on the test set was the deep neural network. Also, we proposed a method for forming the space of thermometric features, which at the same time ensures a sufficiently high efficiency of the classification algorithms. More importantly, the model is inherently able to provide an explanation of the diagnostic solution.

AL and VL are grateful to Russian Foundation for Basic Research (grant No RFBR 19-01-00358) for the financial support of the development of mathematical models for early diagnosis of breast cancer.

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Authors and Affiliations

  1. Volgograd State University, Volgograd, Russia

    Vladislav Levshinskii & Alexander Losev

  2. University of Edinburgh, Edinburgh, UK

    Christoforos Galazis & Igor Goryanin

  3. Medical Microwave Radiometry Ltd., Edinburgh, UK

    Lev Ovchinnikov & Sergey Vesnin

  4. Okinawa Institute of Science and Technology, Okinawa, Japan

    Lev Ovchinnikov & Igor Goryanin

Authors
  1. Vladislav Levshinskii
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  2. Christoforos Galazis
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  3. Lev Ovchinnikov
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  4. Sergey Vesnin
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  5. Alexander Losev
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  6. Igor Goryanin
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Correspondence to Igor Goryanin .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Roque

  2. Lodz University of Technology, Łódź, Poland

    Arkadiusz Tomczyk

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. University of Bremen, Bremen, Germany

    Felix Putze

  5. University of West Bohemia, Pilsen, Czech Republic

    Roman Moucek

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Levshinskii, V., Galazis, C., Ovchinnikov, L., Vesnin, S., Losev, A., Goryanin, I. (2020). Application of Data Mining and Machine Learning in Microwave Radiometry (MWR). In: Roque, A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2019. Communications in Computer and Information Science, vol 1211. Springer, Cham. https://doi.org/10.1007/978-3-030-46970-2_13

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