Browsing by Author "Tastembekov A."

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    COMPUTER ANALYSIS OPTICAL COHERENCE TOMOGRAPHY IMAGES BY USING UNSUPERVISED MACHINE LEARNING ALGORITHM
    (СДУ хабаршысы - 2020, 2020) Amirgaliev Y. ; Tastembekov A. ; Bertailak Sh.
    Abstract. In recent years, computer image analysis has been developing rapidly. In the field of medicine has been identified to a new level that has greatly helped for the diagnostic system. There are many information systems in the field of ophthalmology and cardiology. Advanced technologies not only accelerate the work of doctors but also help to diagnose the disease in a timely manner and prescribe the treatment. In this research paper was carried out an analysis of the machine learning algorithm using a database of tomographic images of blood vessels in the eye system. Were studied the used methods for calculating several reasons in order to select a specific model, methods for calculating its properties and advantages. The main goal of this research is that doctors can not only check the current condition of the patient’s eye but also diagnose certain diseases, such as diabetes and anemia
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    Development of algorithms and software for recognition of airway inflammation based on image processing
    (Faculty of Engineering and Natural Science, 2020) Tastembekov A.
    Every person is always happy to make sure that everything is good with his body and that he is not ill. It is the desire of doctors and their patients not to face the consequences of diseases, but to timely detect or prevent them makes diagnostic procedures so popular. The X-ray is one of the most common methods for diagnosing various lung diseases and is prescribed much more often than other types of examinations - magnetic resonance imaging or computed tomography. In this thesis, we develop an algorithm and application to analyze X-ray images to predict airway inflammation. We preprocess the data by cropping the lung area to have more accurate results at analysis. Our goal is to help the medical system in a mass preliminary inspection during the SARS-COV-2 pandemic. The results obtained in this work can be used in the future to help doctors to conduct the examination more efficiently, which will reduce the time spent on each person.