[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123767-en":3,"doc-seo-123767-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123767,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Classification of Pathologies on Digital Chest Radiographs Using Machine Learning Methods","Research and development focuses on automatically classifying diverse pathologies in digital chest radiographs using two machine learning approaches: eXtreme gradient boosting (XGBoost) and a deep convolutional neural network residual network (ResNet50). The study compiles a large dataset covering multiple clinical cases and pathology classes, preprocesses images, and trains both models. Model performance is evaluated on test data with quality metrics and comparative analysis to assess accuracy, reliability, sensitivity, and specificity for potential clinical decision support in radiology.","Classification of pathologies on digital chest radiographs using  \nmachine learning methods  \nMurat Aitimov1, Ainur Shekerbek2, Igor Pestunov3, Galitdin Bakanov4, Aiymkhan Ostayeva5, Gulzat Ziyatbekova6, Saule Mediyeva7, Gulmira Omarova2  \n1Kyzylorda Regional Branch at the Academy of Public Administration under the President of the Republic of Kazakhstan, Kyzylorda,  \nRepublic of Kazakhstan  \n2Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana,  \nRepublic of Kazakhstan  \n3Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia 4Faculty of Natural Sciences, Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Republic of Kazakhstan 5Educational Program Informatics and Information and Communication Technologies, Korkyt Ata Kyzylorda University, Kyzylorda,  \nRepublic of Kazakhstan  \n6Department of Information Systems, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, Republic of  \nKazakhstan  \n7Department of Language Development Center, Karaganda State Medical University, Karaganda, Republic of Kazakhstan  \nArticle history:  \nReceived Sep 13, 2023 Revised Dec 1, 2023 Accepted Dec 13, 2023  \nKeywords:  \neXtreme gradient boosting Machine learning Medical imaging texture Pathology  \nResidual network X-rays  \nCorresponding Author:  \nThis 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.  \nThis is an open access article under the CC BY-SA license.  \nAinur Shekerbek  \nDepartment of Information Systems, Faculty Information Technology, L. N. Gumilyov Eurasian National University  \n010000 Astana, Republic of Kazakhstan  \nEmail: [shekerbek80@mail.ru](shekerbek80@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nModern medical care is faced with the need to effectively and accurately detect pathologies on chest X-rays. Digital radiographs provide extensive information about the state of the respiratory and cardiovascular systems, but their analysis requires high qualifications and time on the part of medical specialists [1] . In this work, machine learning methods became a key tool to automate and improve the process of classifying pathologies in radiographs. One of the key challenges in this field is the automatic classification of pathologies  \nin chest X-rays [2]–[4] with high accuracy and efficiency. Correct and rapid diagnosis of chest diseases such as lung cancer, tuberculosis, and other pathologies is critical for successful treatment and increasing patient survival. Machine learning techniques such as the eXtreme gradient boosting (XGBoost) algorithm and deep neural networks provide new opportunities for the automatic diagnosis and classification of ","cbCaidHIJU8DgGjW","https://ap.wps.com/l/cbCaidHIJU8DgGjW","pdf",457077,1,7,"English","en",105,"# ABSTRACT\n# INTRODUCTION\n## Objective and challenges in chest X-ray pathology classification\n## Comparison of XGBoost and ResNet50\n## Related self-supervised and self-guided learning approaches","[{\"question\":\"What methods are used to classify pathologies on digital chest radiographs?\",\"answer\":\"The study develops and trains two approaches: XGBoost and a deep convolutional neural network residual network (ResNet50).\"},{\"question\":\"How is the dataset used in the study?\",\"answer\":\"An extensive dataset of preprocessed digital chest radiographs is compiled, containing varied clinical cases and multiple pathology classes, then split for test evaluation.\"},{\"question\":\"What evaluation criteria are used to compare the models?\",\"answer\":\"Both models are assessed on test data using quality metrics, and the study analyzes accuracy, sensitivity, and specificity to compare performance in clinical context.\"}]","Classification of Pathologies on Digital Chest Radiographs Using Machine Learning Methods | 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