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All Journal International Journal of Electrical and Computer Engineering
Zhanat Manbetova
Saken Seifullin Kazakh Agrotechnical University
Computer Science & IT Electrical & Electronics Engineering

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Detection of chest pathologies using autocorrelation functions Gulzira Abdikerimova; Ainur Shekerbek; Murat Tulenbayev; Svetlana Beglerova; Elena Zakharevich; Gulmira Bekmagambetova; Zhanat Manbetova; Makbal Baibulova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4526-4534

Abstract

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.
Applying textural Law’s masks to images using machine learning Gulzira Abdikerimova; Moldir Yessenova; Akbota Yerzhanova; Zhanat Manbetova; Gulden Murzabekova; Dinara Kaibassova; Roza Bekbayeva; Madina Aldashova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5569-5575

Abstract

Currently, artificial neural networks are experiencing a rebirth, which is primarily due to the increase in the computing power of modern computers and the emergence of very large training data sets available in global networks. The article considers Laws texture masks as weights for a machine-learning algorithm for clustering aerospace images. The use of Laws texture masks in machine learning can help in the analysis of the textural characteristics of objects in the image, which are further identified as pockets of weeds. When solving problems in applied areas, in particular in the field of agriculture, there are often problems associated with small sample sizes of images obtained from aerospace and unmanned aerial vehicles and insufficient quality of the source material for training. This determines the relevance of research and development of new methods and algorithms for classifying crop damage. The purpose of the work is to use the method of texture masks of Laws in machine learning for automated processing of high-resolution images in the case of small samples using the example of problems of segmentation and classification of the nature of damage to crops.
Co-Authors Ainur Shekerbek Akbota Yerzhanova Dinara Kaibassova Elena Zakharevich Gulden Murzabekova Gulmira Bekmagambetova Gulzira Abdikerimova Gulzira Abdikerimova Madina Aldashova Makbal Baibulova Moldir Yessenova Murat Tulenbayev Roza Bekbayeva Svetlana Beglerova