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Cybernetics And Systems Analysis
International Theoretical Science Journal
Volume 59 >>> № 2 MARCH — APRIL 2023
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UDC 004.048+616-079.4
V. Babenko1, Ie. Nastenko2, V. Pavlov3, O. Horodetska4,
I. Dykan5, B. Tarasiuk6, V. Lazoryshinets7


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

 vbabenko2191@gmail.com

2 National Technical University “Igor Sikorsky Kyiv Polytechnic Institute,” Kyiv, Ukraine

 nastenko.e@gmail.com

3 National Technical University “Igor Sikorsky Kyiv Polytechnic Institute,” Kyiv, Ukraine

 pavlov.vladimir264@gmail.com

4 National Technical University “Igor Sikorsky Kyiv Polytechnic Institute,” Kyiv, Ukraine

 o.nosovets@gmail.com

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

 irinadykan@gmail.com

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

 btarasyuk13@gmail.com

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

 lazorch@ukr.net

PATHOLOGY CLASSIFICATION FROM MEDICAL IMAGES BY THE ALGORITHM
OF RANDOM FOREST OF OPTIMAL-COMPLEXITY TREES

Abstract. 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 features’ ensembles in the construction of forest trees. The principles of the group method of data handling are used to optimize the trees’ structure. Optimization of the tree voting procedure in the forest is implemented by the analytic hierarchy process. Examples of using the proposed algorithm to identify pathologies in medical images and the classification results as compared with other known analogs are presented.

Keywords: pathology classification, medical images, random forest, genetic algorithm, group method of data handling, analytic hierarchy process.


full text

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