1
Master's Degree, Department of Biomedical Engineering-Biomechanics, Faculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
2
Assistant Professor, Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
In automated cell counting devices, the performance of the counting channel can be significantly affected under problematic conditions, such as platelet aggregation, leading to inaccuracies in key blood parameter measurements. Given the limitations of existing algorithms in addressing these challenges, this study proposes an enhanced algorithm to improve the counting accuracy of critical blood components, particularly platelets and hematocrit, within the counting chamber. To achieve this, a hybrid approach integrating two computational models was implemented, demonstrating an improvement in overall counting performance. Among the tested optimization techniques, the Satin Bowerbird Optimization (SBO) Algorithm yielded superior results, outperforming other methods in terms of prediction accuracy. While the Biogeography-Based Optimization (BBO) Algorithm and the Teaching-Learning-Based Optimization (TLBO) Algorithm exhibited higher accuracy for certain blood parameters compared to the SBO Algorithm, the SBO Algorithm achieved the highest number of correct predictions across all parameters. In contrast, the Particle Swarm Optimization (PSO) and Firefly (FA) Algorithms failed to produce reliable results. The findings highlight the effectiveness of the proposed algorithm in enhancing the robustness and precision of blood parameter quantification, making it a promising approach for improving automated cell counting in clinical and laboratory applications.
Binnicker, M. J. (2020). Emergence of a novel coronavirus disease (COVID-19) and the importance of diagnostic testing: Why partnership between clinical laboratories, public health agencies, and industry is essential to control the outbreak. Clinical Chemistry. https://doi.org/10.1093/clinchem/hvaa071
D'Cruz, R. J., Currier, A., & Sampson, V. B. (2020). Laboratory testing methods for novel severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Frontiers in Cell and Developmental Biology, 8. https://doi.org/10.3389/fcell.2020.00468
Rubin, D., Jarrah, A. A., Li, K., LaRose, S. L., Monk, A. D., Ali, A., Spendley, L., Nikiforow, S., Jacobson, C., & Vaitkevicius, H. (2020). Clinical predictors of neurotoxicity after chimeric antigen receptor T-cell therapy. JAMA Neurology. https://doi.org/10.1001/jamaneurol.2020.2703
Thomas, T. A., Stefanoni, D., Dzieciatkowska, M., Issaian, A., Nemkov, T., Hill, R. C., Francis, R. O., Hudson, K. E., Buehler, P. W., Zimring, J. C., Hod, E. A., Hansen, K. C., Spitalnik, S. L., & D'Alessandro, A. (2020). Evidence of structural protein damage and membrane lipid remodeling in red blood cells from COVID-19 patients. Journal of Proteome Research, 19(12), 4455-4469. https://doi.org/10.1021/acs.jproteome.0c00606
Nader, E., Romana, M., & Connes, P. (2020). The red blood cell-inflammation vicious circle in sickle cell disease. Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.00454
Hausfater, P., Boter, N. R., Indiano, C. M., Abreu, M. C. de., Marin, A. M., Pernet, J., Quesada, D., Castro, I., Careaga, D., Arock, M., Tejidor, L., & Velly, L. (2021). Monocyte distribution width (MDW) performance as an early sepsis indicator in the emergency department: Comparison with CRP and procalcitonin in a multicenter international European prospective study. Critical Care, 25, 36. https://doi.org/10.1186/s13054-021-03622-5
Zaninetti, C., & Greinacher, A. (2020). Diagnosis of inherited platelet disorders on a blood smear. Journal of Clinical Medicine, 9(2), 539. https://doi.org/10.3390/jcm9020539
Cheng, Y., Wang, K., Xu, H., Li, T., Jin, Q., & Cui, D. (2021). Recent developments in sensors for wearable device applications. Analytical and Bioanalytical Chemistry, 413, 6037-6057. https://doi.org/10.1007/s00216-021-03602-2
Poljak, M., Korva, M., Gašper, N. K., Komloš, K. F., Sagadin, M., Uršič, T., Županc, T. A., & Petrovec, M. (2020). Clinical evaluation of the cobas SARS-CoV-2 test and a diagnostic platform switch during 48 hours in the midst of the COVID-19 pandemic. Journal of Clinical Microbiology, 58. https://doi.org/10.1128/JCM.00599-20
Thomas, T. A., Stefanoni, D., Dzieciatkowska, M., Issaian, A., Nemkov, T., Hill, R. C., Francis, R. O., Hudson, K. E., Buehler, P. W., Zimring, J. C., Hod, E. A., Hansen, K. C., Spitalnik, S. L., & D'Alessandro, A. (2020). Evidence for structural protein damage and membrane lipid remodeling in red blood cells from COVID-19 patients. medRxiv: the preprint server for health sciences. https://doi.org/10.1101/2020.06.29.20142703
Sobczyńska-Malefora, A., Delvin, E., McCaddon, A., Ahmadi, K., & Harrington, D. (2021). Vitamin B12 status in health and disease: A critical review. Diagnosis of deficiency and insufficiency - clinical and laboratory pitfalls. Critical Reviews in Clinical Laboratory Sciences, 58(6), 399-429. https://doi.org/10.1080/10408363.2021.1885339
Stegeman, I., Ochodo, E., Guleid, F., Holtman, G., Yang, B., Davenport, C., Deeks, J., Dinnes, J., Dittrich, S., Emperador, D. M., Hooft, L., Spijker, R., Takwoingi, Y., Van den Bruel, A., Wang, J., Langendam, M., Verbakel, J., & Leeflang, M. (2020). Routine laboratory testing to determine if a patient has COVID‐19. The Cochrane Database of Systematic Reviews. https://doi.org/10.1002/14651858.CD013787
Gould, M. K., Huang, B. Z., Tammemagi, M. C., Kinar, Y., & Shiff, R. (2021). Machine learning for early lung cancer identification using routine clinical and laboratory data. American Journal of Respiratory and Critical Care Medicine. https://doi.org/10.1164/rccm.202007-2791OC
Nazari, S. (2013). Textbook of blood devices. Shahid Beheshti University of Medical Sciences and Health Services, Tehran.
Goodarzi, A. (2015). Platelets and megakaryocytes (Vol. 1). Tehran: Zehd Publications.
Yampri, C., Pintavirooj, S., Daochai, S., & Teartulakarn. (2006). White blood cell classification based on the combination of eigen cell and parametric feature detection. In Proceedings of the First IEEE Conference on Industrial Electronics and Applications (pp. 1-4). https://doi.org/10.1109/ICIEA.2006.257341
Sabino, L. d. F., Costa, E. G., Rizzatti, M. A. Z., & Zago. (2004). A texture approach to leukocyte recognition. Real-Time Imaging, 10(3), 205-216. https://doi.org/10.1016/j.rti.2004.02.007
Dorini, R., Minetto, N. J., & Leite. (2007). White blood cell segmentation using morphological operators and scale-space analysis. In Proceedings of the XXth Brazilian Symposium on Computer Graphics and Image Processing (pp. 294-304). https://doi.org/10.1109/SIBGRAPI.2007.33
Sadeghian, Z., Seman, A. R., Ramli, B. H. A., Kahar, M. N., & Saripan. (2009). A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol. Proced. Online, 11, 196-206. https://doi.org/10.1007/s12575-009-9011-2
Ahmadi,N. and Asyabi,S. (2021). Improvement of the Performance of Cell Counter Devices Using a Proposed Model Combining Multilayer Perceptron Neural Networks. Transactions on Machine Intelligence, 4(4), 182-190. doi: 10.47176/TMI.2021.182
MLA
Ahmadi,N. , and Asyabi,S. . "Improvement of the Performance of Cell Counter Devices Using a Proposed Model Combining Multilayer Perceptron Neural Networks", Transactions on Machine Intelligence, 4, 4, 2021, 182-190. doi: 10.47176/TMI.2021.182
HARVARD
Ahmadi N., Asyabi S. (2021). 'Improvement of the Performance of Cell Counter Devices Using a Proposed Model Combining Multilayer Perceptron Neural Networks', Transactions on Machine Intelligence, 4(4), pp. 182-190. doi: 10.47176/TMI.2021.182
CHICAGO
N. Ahmadi and S. Asyabi, "Improvement of the Performance of Cell Counter Devices Using a Proposed Model Combining Multilayer Perceptron Neural Networks," Transactions on Machine Intelligence, 4 4 (2021): 182-190, doi: 10.47176/TMI.2021.182
VANCOUVER
Ahmadi N., Asyabi S. Improvement of the Performance of Cell Counter Devices Using a Proposed Model Combining Multilayer Perceptron Neural Networks. Trans. Mach. Intell., 2021; 4(4): 182-190. doi: 10.47176/TMI.2021.182
