Classification of pathologies on digital chest radiographs using machine learning methods
Aitimov M. Shekerbek A. Pestunov I. Bakanov G. Ostayeva A. Ziyatbekova G. Mediyeva S. Omarova G.
2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 21899 - 1905 pp.
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
eXtreme gradient boosting , Machine learning , Medical imaging texture , Pathology , Residual network , X-rays
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Kyzylorda Regional Branch, the Academy of Public Administration under the President of the Republic of Kazakhstan, Kyzylorda, Kazakhstan
Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Federal Research Center for Information and Computational Technologies, Novosibirsk, Russian Federation
Faculty of Natural Sciences, Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Kazakhstan
Educational Program Informatics and Information and Communication Technologies, Korkyt Ata Kyzylorda University, Kyzylorda, Kazakhstan
Department of Information Systems, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of Language Development Center, Karaganda State Medical University, Karaganda, Kazakhstan
Kyzylorda Regional Branch
Department of Information Systems
Federal Research Center for Information and Computational Technologies
Faculty of Natural Sciences
Educational Program Informatics and Information and Communication Technologies
Department of Information Systems
Department of Language Development Center
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