Application of mathematical methods and machine learning algorithms for classification of X-ray images
DOI:
https://doi.org/10.15587/1729-4061.2022.259710Keywords:
mathematical methods, machine learning, neural networks, pattern recognition, medical image processing, artificial intelligenceAbstract
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 %
Supporting Agency
- Scientific supervisor Gulzira Abdikerimova and doctoral student Shekerbek Ainur express their gratitude for the guidance on the topic of their scientific work.
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Copyright (c) 2022 Ainur Shekerbek, Sandugash Serikbayeva, Murat Tulenbayev, Galitdin Bakanov, Svetlana Beglerova, Anastassiya Makovetskaya
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