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Classification of Pathologies on Medical Images Using the Algorithm of Random Forest of Optimal-Complexity Trees

  • NEW MEANS OF CYBERNETICS, INFORMATICS, COMPUTER ENGINEERING, AND SYSTEMS ANALYSIS
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Cybernetics and Systems Analysis Aims and scope

The authors propose an approach to the construction of classifiers in the class of Random Forest algorithms. A genetic algorithm is used to determine the optimal combination and composition of ensembles of features in the construction of forest trees. The principles of the group method of data handling are used to optimize the structure of the trees. Optimization of the tree voting procedure in the forest is implemented by the analytic hierarchy process. Examples of the use of the proposed algorithm for the detection of pathologies on medical images are provided, as well as the classification results in comparison with other known analogs.

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Authors and Affiliations

  1. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

    V. Babenko, Ie. Nastenko, V. Pavlov & O. Horodetska

  2. Institute of Nuclear Medicine and Radiation Diagnostics, National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine

    I. Dykan & B. Tarasiuk

  3. Amosov National Institute of Cardiovascular Surgery, National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine

    V. Lazoryshinets

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  1. V. Babenko
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  2. Ie. Nastenko
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Correspondence to V. Babenko.

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Translated from Kibernetyka ta Systemnyi Analiz, No. 2, March–April, 2023, pp. 190–202.

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Babenko, V., Nastenko, I., Pavlov, V. et al. Classification of Pathologies on Medical Images Using the Algorithm of Random Forest of Optimal-Complexity Trees. Cybern Syst Anal 59, 346–358 (2023). https://doi.org/10.1007/s10559-023-00569-z

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  • DOI: https://doi.org/10.1007/s10559-023-00569-z

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