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

Vladislav Levshinskii, Christoforos Galazis, Lev Ovchinnikov, Sergey Vesnin, Alexander Losev, Igor Goryanin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationBiomedical Engineering Systems and Technologies
EditorsAna Roque, Arkadiusz Tomczyk, Elisabetta De Maria, Felix Putze, Roman Moucek, Ana Fred, Hugo Gamboa
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages24
ISBN (Electronic)978-3-030-46970-2
ISBN (Print)978-3-030-46969-6
Publication statusPublished - 6 May 2020
Event12th International Joint Conference on Biomedical Engineering Systems and Technologies - Prague, Czech Republic
Duration: 22 Feb 201924 Feb 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference12th International Joint Conference on Biomedical Engineering Systems and Technologies
Abbreviated titleBIOSTEC 2019
Country/TerritoryCzech Republic
Internet address

Keywords / Materials (for Non-textual outputs)

  • Microwave radiometry
  • Breast cancer
  • Diagnostic system
  • Machine learning
  • Neural network
  • Rule-based classification
  • Cascade Correlation Neural Network
  • Convolutional Neural Network
  • Random Forest
  • Support Vector Machine


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