Effective detection of breast pathology using machine learning methods
Orazayeva A. Tussupov J. Shangytbayeva G. Galymova A. Zhunissova U. Tergeussizova A. Tleubayeva A. Kenzhebayeva Z.
October 2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 55593 - 5600 pp.
This 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.
Breast pathologies , convolutional neural network , Deep learning , Faster region-based , Mammography images , You only look once
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Department of Information Systems, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Computer Science and Information Technology, Faculty of Physics and Mathematics, K. Zhubanov Aktobe Regional University, Aktobe, Kazakhstan
Department of Public Health and Informatics, Semipalatinsk State Medical University, Semey, Kazakhstan
Department of Biostatistics, Bioinformatics, and Information Technologies, Astana Medical University, Astana, Kazakhstan
Department of Cyber Security, Almaty University of Energy and Communications named after Gumarbek Daukeev, Almaty, Kazakhstan
Department of Computer Engineering, Astana IT University, Astana, Kazakhstan
Department of Computer Science, Caspian University of Technology and Engineering named after Sh. Yessenov, Aktau, Kazakhstan
Department of Information Systems
Department of Computer Science and Information Technology
Department of Public Health and Informatics
Department of Biostatistics
Department of Cyber Security
Department of Computer Engineering
Department of Computer Science
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