Abstract
The method of microwave radiometry is one of the areas of medical diagnosis of breast cancer. It is based on analysis of the spatial distribution of internal and surface tissue temperatures, which are measured in the microwave (RTM) and infrared (IR) ranges. Complex mathematical and computer models describing complex physical and biological processes within biotissue increase the efficiency of this method. Physical and biological processes are related to temperature dynamics and microwave electromagnetic radiation. Verification and validation of the numerical model is a key challenge to ensure consistency with medical big data. These data are obtained by medical measurements of patients. We present an original approach to verification and validation of simulation models of physical processes in biological tissues. Our approach is based on deep analysis of medical data and we use machine learning algorithms. We have achieved impressive success for the model of dynamics of thermal processes in a breast with cancer foci. This method allows us to carry out a significant refinement of almost all parameters of the mathematical model in order to achieve the maximum possible adequacy.
AL and VL are grateful to Russian Science Foundation (grant RFBR No. 19-01-00358) for the financial support of the development of mathematical models for early diagnosis of breast cancer. MP is grateful to RFBR and the government of Volgograd region according to the research project No. 19-47-343008 for the financial support. AK acknowledges Ministry of Science and Higher Education of the Russian Federation (government task, project No. 2.852.2017/4.6) for the financial support of the development of the software and numerical simulations.
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Levshinskii, V., Polyakov, M., Losev, A., Khoperskov, A.V. (2019). Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis. In: Kravets, A., Groumpos, P., Shcherbakov, M., Kultsova, M. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2019. Communications in Computer and Information Science, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-29750-3_35
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