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Abstract
Analysis is the basis of diagnosis in medical practice. For these purposes, one important source is digital images. Based on this, we look at the digital image of the blood smear. These data allow us to consider the possibility of diagnosing various diseases. Particular attention in the work is paid to certain methods of digital image analysis. The choice of such methods is based on the analysis task. The article presents examples of digital images of a blood smear and the results of their processing.
References
De Bruijne, M. (2016). Machine learning approaches in medical image analysis: From detection to diagnosis. Medical image analysis, 33, 94-97.
Younis, Y. S., & et al.. (2022). Early diagnosis of breast cancer using image processing techniques. Journal of Nanomaterials, 2022, 1-6.
Saw, S. N., & Ng, K. H. (2022). Current challenges of implementing artificial intelligence in medical imaging. Physica Medica, 100, 12-17.
Chen, H., & et al.. (2022). Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review. NPJ digital medicine, 5(1), 156.
Boboyorov Sardor Uchqun o‘g‘li, Lyubchenko Valentin, & Lyashenko Vyacheslav. (2023). Image Processing Techniques as a Tool for the Analysis of Liver Diseases. Journal of Universal Science Research, 1(8), 223–233.
Boboyorov Sardor Uchqun o‘g‘li, Lyubchenko Valentin, & Lyashenko Vyacheslav. (2023). Pre-processing of digital images to improve the efficiency of liver fat analysis. Multidisciplinary Journal of Science and Technology, 3(1), 107–114.
Gichoya, J. W., & et al.. (2022). AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health, 4(6), e406-e414.
Lyashenko, V., & et al.. (2016). The Methodology of Image Processing in the Study of the Properties of Fiber as a Reinforcing Agent in Polymer Compositions. International Journal of Advanced Research in Computer Science, 7(1), 15-18.
Lyubchenko, V., & et al.. (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.
Al-Sharo, Y. M., & et al.. (2021). Neural Networks As A Tool For Pattern Recognition of Fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.
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.
Tahseen A. J. A., & et al.. (2023). Binarization Methods in Multimedia Systems when Recognizing License Plates of Cars. International Journal of Academic Engineering Research (IJAER), 7(2), 1-9.
Orobinskyi, P., & et al.. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.
Attar, H., & et al.. (2022). Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging. Computational Intelligence and Neuroscience, 2022.
Babker, A. M., & Di Elnaim, E. O. (2020). Hematological changes during all trimesters in normal pregnancy. Journal of Drug Delivery and Therapeutics, 10(2), 1-4.
Babker, A. M. (2020). The role of Inherited Blood Coagulation Disorders in Recurrent Miscarriage Syndrome. Journal of Critical Reviews, 7(1), 16-20.
Eldour, A. A. A., & et al.. (2015). Red cell alloimmunization in blood transfusion dependent Patients with Sickle Cell Disease in El-Obied city, Sudan. IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), 14(12), 137-141.
Abbas, N., & et al.. (2015). Nuclei segmentation of leukocytes in blood smear digital images. Pakistan journal of pharmaceutical sciences, 28(5), 1801-1806.
Gering, E., & Atkinson, C. T. (2004). A rapid method for counting nucleated erythrocytes on stained blood smears by digital image analysis. Journal of Parasitology, 90(4), 879-881.
Dvanesh, V. D., & et al.. (2018, March). Blood cell count using digital image processing. In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) (pp. 1-7). IEEE.
Sadeghian, F., & et al.. (2009). A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biological procedures online, 11, 196-206.
Nugroho, H. A., & et al.. (2019). Identification of Plasmodium falciparum and Plasmodium vivax on digital image of thin blood films. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 933-944.
Kaewkamnerd, S., & et al.. (2012, December). An automatic device for detection and classification of malaria parasite species in thick blood film. In Bmc Bioinformatics (Vol. 13, No. 17, pp. 1-10). BioMed Central.
Ritter, N., & Cooper, J. (2007). Segmentation and border identification of cells in images of peripheral blood smear slides. In 30th Australasian Computer Science Conference, ACSC 2007.
Sholeh, F. I. (2013, September). White blood cell segmentation for fresh blood smear images. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 425-429). IEEE.
Boboyorov Sardor Uchqun o'g'li, Sinelnikova Tetiana, Zeleniy Oleksandr, & Lyashenko Vyacheslav. (2023). Color-aware digital image segmentation procedure as a tool for studying fatty liver disease. Journal of Universal Science Research, 1(9), 431–441.
Lyashenko, V., & et al.. (2021). Wavelet ideology as a universal tool for data processing and analysis: some application examples. International Journal of Academic Information Systems Research (IJAISR), 5(9), 25-30
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