Abstract
Respiratory diseases are one of the primary causes of death in today’s population, and early detection of lung disorders has always been and continues to be critical. In this sense, it is critical to evaluate the condition of the lungs on a regular basis in order to avoid disease or detect it before it does substantial harm to human health. As the most popular and readily available research tool in diagnosis, radiography is critical. Despite all of the benefits of this technology, diagnosing sickness symptoms from photos is a challenging task that necessitates the involvement of highly experienced specialists as well as significant time investment. The difficulty arises from the incompleteness and inaccuracy of the initial data, particularly the presence of numerous image distortions such as excessive exposure, the presence of foreign objects, and so on. The U-net technique was used to do early processing of CT images of the lungs using a neural network during the research. The current status of study in the field of X-ray and CT image identification employing in-depth training methodologies demonstrated that pathological process recognition is one of the most significant tasks of processing today.
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Acknowledgment
This work was funded by Committee of Science of Republic of Kazakhstan AP09260767 “Development of an intellectual information and analytical system for assessing the health status of students in Kazakhstan” (2021–2023).
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Sarsembayeva, T., Mansurova, M., Shomanov, A., Sarsembayev, M., Sagyzbayeva, S., Rakhimzhanov, G. (2022). Pre-processing of CT Images of the Lungs. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_39
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DOI: https://doi.org/10.1007/978-3-031-21967-2_39
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