[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120003-en":3,"doc-seo-120003-105":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},120003,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","Effective Detection of Breast Pathology Using Machine Learning Methods - Abstract","Research develops and evaluates machine learning methods for effective identification of breast pathologies in mammography images. The study reviews existing diagnostic approaches, then applies YOLOv8 and Faster R-CNN architectures to build detection models. Identified breast pathologies are analyzed and classified at six severity levels, supporting more accurate estimation of disease progression and evidence for more individualized treatment planning. Experimental results show high image-processing accuracy and speed, enabling fast, reliable screening and improved decision support for clinical diagnosis and therapy.","Effective detection of breast pathology using machine learning  \nmethods  \nAinur Orazayeva1, Jamalbek Tussupov1, Gulmira Shangytbayeva2, Assem Galymova3, Ulzhalgas Zhunissova4, Aliya Tergeussizova5, Arailym Tleubayeva6, Zhanat Kenzhebayeva7  \n1Department of Information Systems, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana,  \nRepublic of Kazakhstan  \n2Department of Computer Science and Information Technology, Faculty of Physics and Mathematics, K. Zhubanov Aktobe Regional  \nUniversity, Aktobe, Republic of Kazakhstan  \n3Department of Public Health and Informatics, Semipalatinsk State Medical University, Semey, Republic of Kazakhstan 4Department of Biostatistics, Bioinformatics, and Information Technologies, Astana Medical University, Astana,  \nRepublic of Kazakhstan  \n5Department of Cyber Security, Almaty University of Energy and Communications named after Gumarbek Daukeev, Almaty,  \nRepublic of Kazakhstan  \n6Department of Computer Engineering, Astana IT University, Astana, Republic of Kazakhstan 7Department of Computer Science, Caspian University of Technology and Engineering named after Sh. Yessenov, Aktau,  \nRepublic of Kazakhstan  \nArticle history:  \nReceived Mar 12, 2024 Revised Jun 20, 2024 Accepted Jul 2, 2024  \nKeywords:  \nBreast pathologies  \nDeep learning  \nFaster region-based convolutional neural network Mammography images You only look once  \nCorresponding Author:  \nThis work is devoted to the research and development of methods for effectively identifying breast pathologies using modern machine learning technologies, such as you only look once (YOLOv8) and faster region-based convolutional neural network (R-CNN) . The paper presents an analysis of existing approaches to the diagnosis of breast diseases and an assessment of their effectiveness. YOLOv8 and Faster R-CNN architectures are then applied to create pathology detection models in mammography images. The work analyzed and classified identified breast pathologies at six levels, taking into account different degrees of severity and characteristics of the diseases. This approach allows for more accurate determination of disease progression and provides additional data for more individualized treatment planning. Classification results at various levels can improve the quality of medical decisions and provide more accurate information to doctors, which in turn improves the overall efficiency of diagnosis and treatment of breast diseases. Experimental results demonstrate high accuracy and speed of image processing, providing fast and reliable detection of potential breast pathologies. The data obtained confirm the effectiveness of the use of machine learning algorithms in the field of medical diagnostics, providing prospects for the further development of automated systems for detecting breast diseases in order to improve early diagnosis and treatment efficiency.  \nThis is an open access article under the CC BY-SA license.  \nJamalbek Tussupov  \nDepartment of Information Systems, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University  \n010000 Astana, Republic of Kazakhstan  \nEmail: [tussupov@mail.ru](tussupov@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nModern medical diagnostic technologies [1]–[3] are rapidly integrating the capabilities and principles of machine learning to improve the accuracy and efficiency of disease detection [4]–[7] . One of  \nthe urgent tasks in the field of women's health is the diagnosis [8] of breast pathologies [9]–[12], among which are various forms of cancer [13], [14] and other dysfunctions. This paper examines the study of effective methods for identifying breast pathologies and is of particular importance. Machine learning, in particular the you only look once (YOLOv8) [15]–[17] and faster region-based convolutional neural network (R-CNN) [18], [19] algorithms, provide promising tools for automating the process of analyzing medical images. These methods can ","cbCaiseG7V6uyXBH","https://ap.wps.com/l/cbCaiseG7V6uyXBH","pdf",557124,1,8,"English","en",105,"# Introduction\n## Machine learning for breast pathology diagnosis\n# Method Development and Evaluation\n## YOLOv8 and Faster R-CNN models\n## Severity-level classification for mammography\n# Impact on Clinical Decisions\n## Early detection and individualized treatment planning","[{\"question\":\"Which machine learning models are used to detect breast pathologies in mammography images?\",\"answer\":\"The study applies you only look once (YOLOv8) and Faster region-based convolutional neural network (Faster R-CNN) architectures to create pathology detection models for mammograms.\"},{\"question\":\"How does the approach classify breast diseases?\",\"answer\":\"Detected breast pathologies are analyzed and classified at six levels, taking into account severity degrees and disease characteristics.\"},{\"question\":\"What performance benefits does the study report?\",\"answer\":\"Experimental results demonstrate high accuracy and fast image-processing speed, supporting quick and reliable detection of potential breast pathologies.\"}]","Effective Detection of Breast Pathology Using Machine Learning Methods - 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