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	Research of machine learning methods in the problem of identification of blood cells</div>

Ekaterina A. Elagina , Margun Alexei A.

2021 , VOLUME 21, NUMBER 6 ( november-december )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2021-21-6-903-911

Research of machine learning methods in the problem of identification of blood cells

E. A. Elagina, A. A. Margun

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Article in Russian

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Elagina E.A., Margun A.A. Research of machine learning methods in the problem of identification of blood cells. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 6, pp. 903–911 (in Russian). doi: 10.17586/2226-1494-2021-21-6-903-911

Abstract

The development of the medical imaging field is becoming a significant challenge due to the growing need for automated, fast, and efficient diagnostics. Traditionally, blood cells are counted by using a hemocytometer along with other laboratory equipment and chemicals, which is a laborious task. The paper investigates the application of machine learning methods to the identification and classification of blood cells, which allow increasing the recognition rate without deteriorating quality. A comparative analysis of methods for solving the problem of recognizing blood cells based on artificial intelligence approaches is carried out. The paper uses support vector machine, k-nearest neighbors’ algorithm, deep learning (convolutional neural network), and forward propagation neural network. A set of images with cell samples was selected as the initial data for comparison. A comparative analysis of the quality of the considered algorithms was performed on a set of training data with more than 3000 images. It is shown that a program that implements artificial intelligence methods provides a cell recognition time within 4-6 seconds when using an office personal computer, which is significantly less than the time spent by medical workers on one study of a biomaterial. The implementation of the presented results makes it possible to automate the process of studying a biomaterial, reduce the time for conducting and obtaining the result of the analysis of whole blood cells (identification and counting), lessen the influence of operator errors on the result, unload computing resources, thereby increases the efficiency of digital medicine.

Keywords: support vector machine, SVM, convolutional neural networks, CNN, machine learning, deep learning

References

Meng N., Lam E.Y., Tsia K.K., So H.K. Large-scale multi-class image-based cell classification with deep learning. IEEE Journal of Biomedical and Health Informatics, 2019, vol. 23, no. 5, pp. 2091–2098. https://doi.org/10.1109/JBHI.2018.2878878
Akrimi J.A., Suliman A., George L.E., Ahmad A.R. Classification red blood cells using support vector machine. Proc. of the 6^th International Conference on Information Technology and Multimedia (ICIM), 2014, pp. 265–269. https://doi.org/10.1109/ICIMU.2014.7066642
Dyrnayev A. Red cells count method on blood smears images. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2011, no. 6(76), pp. 17–22. (in Russian)
Cormen T.H., Leisorson C.E., Rivest R.L. Introduction to Algorithms. MIT Press, 1990, pp. 185–191.
Alam M.M., Islam M.T. Machine learning approach of automatic identification and counting of blood cells. Healthcare Technology Letters, 2019, vol. 6, no. 4, pp. 103–108. https://doi.org/10.1049/htl.2018.5098
Go T., Byeon H., Lee S.J. Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning. Biosensors & Bioelectronics, 2018, vol. 103, pp. 12–18. https://doi.org/10.1016/j.bios.2017.12.020
Huang X., Jiang Y., Liu X., Xu H., Han Z., Rong H., Yang H., Yan M., Yu H. Machine learning based single-frame super-resolution processing for lensless blood cell counting. Sensors (Basel, Switzerland), 2016, vol. 16, no. 11, pp. 1836. https://doi.org/10.3390/s16111836
Rivenson Y., Wu Y., Ozcan A. Deep learning in holography and coherent imaging. Light-Science & Applications, 2019, vol. 8, pp. 85. https://doi.org/10.1038/s41377-019-0196-0
Jo Y., Cho H., Lee S.Y., Choi G., Kim G., Min H.S., Park Y. Quantitative phase imaging and artificial intelligence: A Review. IEEE Journal of Selected Topics in Quantum Electronics, 2019, vol. 25, no. 1, pp. 6800914. https://doi.org/10.1109/JSTQE.2018.2859234
Chernykh E.M., Mikhelev V.M. Computer system for leukocytes classification on blood cell images. Research result. Information technologies, 2019, vol. 4, no. 3, pp. 38–47. (in Russian). https://doi.org/10.18413/2518-1092-2019-4-3-0-6
Belyakov V.K., Sukhenko E.P., Zakharov A.V., Koltsov P.P., Kotovich N.V., Kravchenko A.A., Kutsaev A.S., Osipov A.S., Kuznetsov A.B. On one method of blood cell classification and its software implementation. Software & Systems, 2014, no. 4(108), pp. 46–56. (in Russian). https://doi.org/10.15827/0236-235X.108.046-056
Acevedo A., Merino A., Alférez S., Molina Á., Boldú L., Rodellar J. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in Brief, 2020, vol. 30, pp. 105474. https://doi.org/10.1016/j.dib.2020.105474
VanderPlas J. Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media, 2016, 548 p.
Ioffe S., Szegedy Ch. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of Machine Learning Research, 2015, vol. 37, pp. 448–456.

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