Recognition of Leukocytes on Peripheral Blood and Bone Marrow Smears Using a Neural Network Approach
https://doi.org/10.56304/S2079562922030605
Abstract
The article studies the problem of classification of leukocytes on images of peripheral blood and bone marrow preparations with multiple contact of leukocytes with each other for automated diagnosis of diseases of the hematopoiesis system. The proposed approach is based on the definition of a class of leukocytes by a combination of the K-means method and a convolutional neural network. The application of the Kmeans method is preceded by the implementation of the watershed algorithm with distance conversion. According to the results of the experiment, the accuracy of recognition of lymphoblasts, granulocytes, monocytes, lymphocytes was evaluated. The proposed solutions can later be applied in decision support systems for the diagnosis of acute leukemia.
About the Authors
Yu. V. ZorinRussian Federation
Moscow, 115409
M. A. Avanesov
Russian Federation
Moscow, 115409
A. N. Pronichev
Russian Federation
Moscow, 115409
A. D. Palladina
Russian Federation
Moscow, 115478
References
1. Тупицын Н.Н. Иммунология клеток крови. В кн.: Гематология. Национальное руководство. Под ред. О.А. Рукавицына. 2015. Москва: ГЭОТАРМедиа.
2. Nikitaev V.G. et al. // Bull. Lebedev Phys. Inst. 2016. V. 43 (10). P. 306−308. https://doi.org/10.3103/S1068335616100055
3. Nikitaev V.G. et al. // J. Phys.: Conf. Ser. 2018. V. 945 (1). P. 012008. https://doi.org/10.1088/1742-6596/945/1/012008
4. Nikitaev V.G. et al. // J. Phys.: Conf. Ser. 2018. V. 945 (1). P. 012007. https://doi.org/10.1088/17426596/945/1/012007
5. Anilkumar K.K., Manoj V.J., Sagi T.M. // Biocybern. Biomed. Eng. 2020. V. 40 (4). P. 1406−1420.. https://doi.org/10.1016/j.bbe.2020.08.010
6. Liu H., Cao H., Song E. // J. Med. Syst. 2019. V. 43 (82). P. 1185. https://doi.org/10.1007/s10916-019-1185-9
7. Hegde R.B. et al. // J. Med. Syst. 2018. V. 42 (6). P. 110. https://doi.org/10.1007/s10916-018-0962-1
8. Arslan S., Ozyurek E., Gunduz‐Demir C. // Cytometry Part A. 2014. V. 85 (6) P.480−490. https://doi.org/10.1002/cyto.a.22457
9. Wang Y, Cao Y. // J. Algorithms Comput. Technol. 2019. V. 13. P. 1−10.
10. Cao H., Liu H., Song E. // Biomed. Signal Process. Control. 2018. V. 45. P. 10−21.
11. Baby D. // Int. J. Eng. Technol. 2018. V. 7 (2). P. 155−158. https://doi.org/10.14419/ijet.v7i2.24.12021
12. Miao H., Xiao C. // Comput. Math. Methods Med. 2018. V. 2018. P. 7235795. https://doi.org/10.1155/2018/7235795
13. Liu Y. et al. // IEEE J. Biomed. Health Inform. 2016. V. 21 (6). P. 1644−1655.
14. Loey M., Naman M., Zayed H. // Computers. 2020. V. 9 (2). P. 29. https://doi.org/10.3390/computers9020029
15. Yao X. et al. // Nanomed. Biotechnol. 2021. V. 49 (1). P. 147−155. https://doi.org/10.1080/21691401.2021.1879823
16. Andrade A. R. et al. // Comput. Methods Programs Biomed. 2019. V. 173. P. 1−14. https://doi.org/10.1016/j.cmpb.2019.03.001
Review
For citations:
Zorin Yu.V., Avanesov M.A., Pronichev A.N., Palladina A.D. Recognition of Leukocytes on Peripheral Blood and Bone Marrow Smears Using a Neural Network Approach. Nuclear Physics and Engineering. 2023;14(2):181-184. (In Russ.) https://doi.org/10.56304/S2079562922030605