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Abstract
Identification of diseases and their successful treatment is largely determined by early diagnosis. This allows you to both prevent the development of the disease and get rid of possible negative consequences. Various data can be used for these purposes. We are looking at medical imaging techniques. Microscopic images of the liver, where manifestations of fatty disease are possible, were chosen as the object of study. The paper summarizes the general scheme of the corresponding analysis, and presents the results on real images.
References
Parsa, S. F., & et al.. (2018). Early diagnosis of disease using microbead array technology: a review. Analytica chimica acta, 1032, 1-17.
Poggiali, E., & et al.. (2020). Can lung US help critical care clinicians in the early diagnosis of novel coronavirus (COVID-19) pneumonia?. Radiology, 295(3), E6-E6.
Shernazarov, F., Tohirova, J., & Jalalova, D. (2022). Types of hemorrhagic diseases, changes in newboens, their early diagnosis. Science and innovation, 1(D5), 16-22.
Huang, J., Chen, X., Jiang, Y., Zhang, C., He, S., Wang, H., & Pu, K. (2022). Renal clearable polyfluorophore nanosensors for early diagnosis of cancer and allograft rejection. Nature Materials, 21(5), 598-607.
Dabeer, S., Khan, M. M., & Islam, S. (2019). Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked, 16, 100231.
Liu, X., Song, L., Liu, S., & Zhang, Y. (2021). A review of deep-learning-based medical image segmentation methods. Sustainability, 13(3), 1224.
Vijayalakshmi, A. (2020). Deep learning approach to detect malaria from microscopic images. Multimedia Tools and Applications, 79, 15297-15317.
Lyashenko, V. V., Babker, A. M. A. A., & Kobylin, O. A. (2016). The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Medical Journal, 41(3), 453-463.
Rabotiahov, A., & et al.. (2018). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE
Lyubchenko, V., Matarneh, R., Kobylin, O., & Lyashenko, V. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.
Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.
Mousavi, S. M. H., Lyashenko, V., & Prasath, S. (2019). Analysis of a robust edge detection system in different color spaces using color and depth images. Компьютерная оптика, 43(4), 632-646.
Babker, A., & Lyashenko, V. (2018). Identification of megaloblastic anemia cells through the use of image processing techniques. Int J Clin Biomed Res, 4, 1-5.
Mousavi, S. M. H., Victorovich, L. V., Ilanloo, A., & Mirinezhad, S. Y. (2022, November). Fatty Liver Level Recognition Using Particle Swarm optimization (PSO) Image Segmentation and Analysis. In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 237-245). IEEE.
Duan, Y., Pan, X., Luo, J., Xiao, X., Li, J., Bestman, P. L., & Luo, M. (2022). Association of inflammatory cytokines with non-alcoholic fatty liver disease. Frontiers in immunology, 13, 880298.
Chan, K. E., & et al.. (2022). Global prevalence and clinical characteristics of metabolic-associated fatty liver disease: a meta-analysis and systematic review of 10 739 607 individuals. The Journal of Clinical Endocrinology & Metabolism, 107(9), 2691-2700.
Zhou, L. Q., & et al.. (2019). Artificial intelligence in medical imaging of the liver. World journal of gastroenterology, 25(6), 672.
Gore, J. C. (2020). Artificial intelligence in medical imaging. Magnetic resonance imaging, 68, A1-A4.
Irving, B., Hutton, C., Dennis, A., Vikal, S., Mavar, M., Kelly, M., & Brady, J. M. (2017). Deep quantitative liver segmentation and vessel exclusion to assist in liver assessment. In Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings 21 (pp. 663-673). Springer International Publishing.
Hassan, T. M., Elmogy, M., & Sallam, E. (2015). Medical image segmentation for liver diseases: a survey. International Journal of Computer Applications, 118(19), 38-44.
Mala, K., & Sadasivam, V. (2005, December). Automatic segmentation and classification of diffused liver diseases using wavelet based texture analysis and neural network. In 2005 Annual IEEE India Conference-Indicon (pp. 216-219). IEEE.
Huo, Y., & et al.. (2019). Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Medical physics, 46(8), 3508-3519.
Lupsor-Platon, M., Serban, T., Silion, A. I., Tirpe, G. R., Tirpe, A., & Florea, M. (2021). Performance of ultrasound techniques and the potential of artificial intelligence in the evaluation of hepatocellular carcinoma and non-alcoholic fatty liver disease. Cancers, 13(4), 790.
Lafci, B., & et al.. (2023). Multimodal assessment of non-alcoholic fatty liver disease with transmission-reflection optoacoustic ultrasound. Theranostics, 13(12), 4217.
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