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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vijayaraj, J., et al.: Various segmentation techniques for lung cancer detection using CT images: a review. Turk. J. Comput. Math. Educ. 12(2), 918–928 (2021)
Sluimer, I.C., et al.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Med. Imaging 25, 385–405 (2006)
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)
Baboo, S.S., Iyyapparaj, E.: A classification and analysis of pulmonary nodules in CT images using random forest. In: 2nd International Conference on Inventive Systems and Control (ICISC), pp. 1226–1232 (2018)
Kieu, P.N., et al.: Applying multi-CNNs model for detecting abnormal problem on chest x-ray images. In: 10th International Conference on Knowledge and Systems Engineering (KSE), pp. 300–305 (2018)
Mansurova, M., Sarsenova, L., Kadyrbek, N., Sarsembayeva, T., Tyulepberdinova, G., Sailau, B.: Design and development of student digital health profile, pp. 1–5 (2021). https://doi.org/10.1109/AICT52784.2021.9620459
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).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-21967-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21966-5
Online ISBN: 978-3-031-21967-2
eBook Packages: Computer ScienceComputer Science (R0)