APPLICATION OF MATHEMATICAL METHODS AND MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION OF X-RAY IMAGES

Shekerbek A. Serikbayeva S. Tulenbayev M. Bakanov G. Beglerova S. Makovetskaya A.
2022 Technology Center

Eastern-European Journal of Enterprise Technologies
2022 #3 Issue 2-117 6 - 17 pp.

The relevance of the topic, in particular, if to take one of the information flows, whether it is the action of a human factor or a specific object, then it is true that special processing of the machine learning language and automatic information output significantly optimize human life. With the help of neural networks and their chest radiography is one of the most accessible radiological studies for screening and diagnosis of many lung diseases a special machine learning language is to study the flow of information about it and the same object in real time using neural networks. The article describes the terminology of the problem of X-ray recognition using machine learning methods and algorithms, examines the relevance of the problem, and analyzes the current state of the problem in the field of X-ray recognition. The aspects of the problem being solved, identified during the analysis, in the form of solved problems, approaches, methods, information technologies used, tools and software solutions to the problem are noted The paper is devoted to the description of a modified method of fuzzy clustering of halftone images, which at each iteration performs a dynamic transformation of the source data based on a singular decomposition with automatic selection of the most significant columns of the matrix of left singular vectors. The results of experimental studies were obtained by processing X-ray images. As a result of testing a neural network model, in the output layer of which a sigmoidal activation function was used to activate neurons, and an algorithm was used as an optimization method, the best values of accuracy and completeness were obtained: accuracy – 94.2 During testing, the neural network showed an accuracy of pneumonia recognition equal to 94,27 %

Artificial intelligence , Machine learning , Mathematical methods , Medical image processing , Neural networks , Pattern recognition

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Department of Information Systems, L. N. Gumilyov Eurasian National University, Satpayev str.,2, Nur-Sultan, 010008, Kazakhstan
Department of Information Systems, M. Kh. DulatyTaraz Regional University, Tole bi str.,40, Taraz, 080000, Kazakhstan
Department of Mathematics, KhojaAkhmetYassawi International Kazakh-Turkish University, B.Sattarkhanovave.,29, Turkestan, 161200, Kazakhstan

Department of Information Systems
Department of Information Systems
Department of Mathematics

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