Abstract
Purpose: The paper is devoted to a creation of mathematical model of an artificial neural network for detection of breast cancer based on microwave radiothermometry and anamnesis.
Design/Methodology/Approach: One of the most complex and urgent challenges of modern medicine is arranging of effective mammological screening. The solution to this problem will significantly reduce the mortality from breast cancer by detecting a tumor before clinical manifestation, as well as cut the costs of treating patients. Currently, the main trend in increasing the effectiveness of screening is the transition to modern digital technologies, including the use of methods and algorithms of artificial intelligence.
One of the newest methods for early diagnosis of breast cancer is the method of microwave radiothermometry. Over the past two decades, it has already proved itself in some areas of medicine. In addition, it is one of a few methods recommended for use in mammological screening of women under the age of 40. One of the problems for implementation of this method is a complexity of analysis and interpretation of thermometric data.
Findings: The solution to this problem involves the development of advisory intelligent diagnostic systems. Such systems use the effective classification algorithms, including ones based on artificial neural networks.
Originality/Value: A distinctive feature of the neural network proposed in this study is the use of a specific attribute space as an input layer. This space built on the basis of mathematical functions that simulate the nuances of the behavior of temperature fields in patients of various diagnostic classes.
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Acknowledgments
The study was financially supported by the Russian Foundation for Basic Research, the project “Mathematical Models of Radiation Fields and Analysis of Microwave Radiometry Data in the Early Diagnosis of Breast Cancer” No. 19-01-00358.
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Losev, A.G., Medevedev, D.A., Svetlov, A.V. (2021). Neural Networks in Diagnosis of Breast Cancer. In: Popkova, E.G., Sergi, B.S. (eds) "Smart Technologies" for Society, State and Economy. ISC 2020. Lecture Notes in Networks and Systems, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-59126-7_25
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DOI: https://doi.org/10.1007/978-3-030-59126-7_25
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