Detection of chest pathologies using autocorrelation functions
Abdikerimova G. Shekerbek A. Tulenbayev M. Beglerova S. Zakharevich E. Bekmagambetova G. Manbetova Z. Baibulova M.
August 2023Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2023#13Issue 44526 - 4534 pp.
An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
Chest radiograph , Clustering , Medical imaging , Pathology , Texture
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Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Department of Information Systems, Faculty of Information Technology, M. Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan
Department of Information technology, Faculty of Technology, Kazakh University of Technology and Business, Astana, Kazakhstan
Department of Radio Engineering, Electronics and Telecommunications, Faculty of Energy, Saken Seifullin Kazakh Agrotechnical University, Astana, Kazakhstan
Department of Information Systems
Department of Information Systems
Department of Information technology
Department of Radio Engineering
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