EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 86 (2026) ITM Web Conf., 86 (2026) 01023 References
Open Access
Issue
ITM Web Conf.
Volume 86, 2026
5th International Conference on Current Research in Engineering and Technology (ICCRET-2026)
Article Number 01023
Number of page(s) 13
Section AI & Intelligent Computing
DOI https://doi.org/10.1051/itmconf/20268601023
Published online 05 June 2026
  1. Karakochuk, C. D., Hess, S. Y., Moorthy, D., Namaste, S., Parker, M. E., Rappaport, A. I., ... & HEmoglobin MEasurement (HEME) Working Group. (2019). Measurement and interpretation of hemoglobin concentration in clinical and field settings: a narrative review. Annals of the new York Academy of Sciences, 1450(1), 126–146. [Google Scholar]
  2. Stawschenko, E., Schaller, T., Kern, B., Bode, B., Dörries, F., Kusche-Vihrog, K., ... & Wegerich, P. (2022). Current status of measurement accuracy for total hemoglobin con-centration in the clinical context. Biosensors, 12(12), 1147. [Google Scholar]
  3. Suner, S., Rayner, J., Ozturan, I. U., Hogan, G., Meehan, C. P., Chambers, A. B., ... & Jay, G. D. (2021). Prediction of anemia and estimation of hemoglobin concentration using a smartphone camera. PloS one, 16(7), e0253495. [Google Scholar]
  4. Daves, M., Roccaforte, V., Lombardi, F., Panella, R., Pastori, S., Spreafico, M., ... & Piccin, A. (2024). Modern hematology analyzers: beyond the simple blood cells count (with focus on the red blood cells). Journal of Laboratory and Precision Medicine, 9, 4. [Google Scholar]
  5. Ekong, B., Ekong, O., Silas, A., Edet, A. E., & William, B. (2023). Machine learning approach for classification of sickle cell anemia in teenagers based on Bayesian network. Journal of Information Systems and Informatics, 5(4), 1793–1808. [Google Scholar]
  6. Garcia-Casal, M. N., Dary, O., Jefferds, M. E., & Pasricha, S. R. (2023). Diagnosing anemia: Challenges selecting methods, addressing underlying causes, and implementing actions at the public health level. Annals of the New York Academy of Sciences, 1524(1), 37–50. [Google Scholar]
  7. Zhang, W. T., Chao, T. H. H., Yang, Y., Wang, T. W., Lee, S. H., Oyarzabal, E. A., ... & Shih, Y. Y. I. (2022). Spectral fiber photometry derives hemoglobin concentration changes for accurate measurement of fluorescent sensor activity. Cell reports methods, 2(7). [Google Scholar]
  8. Whitehead Jr, R. D., Mei, Z., Mapango, C., & Jefferds, M. E. D. (2019). Methods and analyzers for hemoglobin measurement in clinical laboratories and field settings. Annals of the New York Academy of Sciences, 1450(1), 147–171. [Google Scholar]
  9. Steinmeyer, Z., Delpierre, C., Soriano, G., Steinmeyer, A., Ysebaert, L., Balardy, L., & Sourdet, S. (2020). Hemoglobin concentration; a pathway to frailty. BMC geriatrics, 20(1), 202. [Google Scholar]
  10. Meegan, J. E., Shaver, C. M., Putz, N. D., Jesse, J. J., Landstreet, S. R., Lee, H. N. R., ... & Bastarache, J. A. (2020). Cell-free hemoglobin increases inflammation, lung apop-tosis, and microvascular permeability in murine polymicrobial sepsis. PLoS One, 15(2), e0228727. [Google Scholar]
  11. Chaudhuri, A. K., Das, S., & Ray, A. (2023). A hybrid feature selection and stacked generalization model to detect breast cancer. In Data-centric AI solutions and emerging technologies in the healthcare ecosystem (pp. 165–183). CRC Press. [Google Scholar]
  12. Hayashi, K., Hitosugi, T., Kawakubo, Y., Kitamoto, N., & Yokoyama, T. (2020). In-fluence of measurement principle on total hemoglobin value. BMC anesthesiology, 20(1), 81. [Google Scholar]
  13. Konig, G., Waters, J. H., Javidroozi, M., Philip, B., Ting, V., Abbi, G., ... & Adams, G. (2018). Real-time evaluation of an image analysis system for monitoring surgical hemoglobin loss. Journal of clinical monitoring and computing, 32(2), 303–310. [Google Scholar]
  14. Chaudhuri, A. K., Sinha, D., Thyagaraj, K. S. (2018). Identification of the recurrence of breast cancer by discriminant analysis. In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2018, Volume 2 (pp. 519–532). Singapore: Springer Singapore. [Google Scholar]
  15. AlSofayan, Y. M., Alsomali, M., Junaidallah, M. A., Bhat, S. A., Jaganathan, P. P., & Almazroua, F. Y. (2018). A comparison study between venous blood gas and complete blood count for the assessment of hemoglobin levels in trauma patients: a single center experience. JK Practitioner, 23, 25–8. [Google Scholar]
  16. Eyth, E., Zubair, M., & Naik, R. (2025). Hemoglobin A1c. In StatPearls [Internet]. StatPearls Publishing. [Google Scholar]
  17. Das, S., Chaudhuri, A. K., Ghosh, P. (2026). Medical Diagnosis Through Improved Feature Selection and Advanced Ensemble Techniques: A Study on Breast Cancer and Chronic Kidney Disease. SN Computer Science, 7(3), 222 [Google Scholar]
  18. Daves, M., Roccaforte, V., Lombardi, F., Panella, R., Pastori, S., Spreafico, M., ... & Piccin, A. (2024). Modern hematology analyzers: beyond the simple blood cells count (with focus on the red blood cells). Journal of Laboratory and Precision Medicine, 9, 4. [Google Scholar]
  19. Boachie, J., Ahiable, D., Ajabuin, L. A., Amissah, R., Asmah-Brown, A., Apalebilah, S., ... & Nyarkoh, J. K. (2025). Diagnostic value of full blood count derived systemic inflammatory biomarkers in malaria infection. Practical Laboratory Medicine, e00494. [Google Scholar]
  20. Raasveld, S. J., De Bruin, S., Reuland, M. C., Van den Oord, C., Schenk, J., Aubron, C., ... & Alkamkhe, A. A. S. (2023). Red blood cell transfusion in the intensive care unit. JAMA, 330(19), 1852–1861. [Google Scholar]
  21. Seo, I. H., & Lee, Y. J. (2022). Usefulness of complete blood count (CBC) to assess car-diovascular and metabolic diseases in clinical settings: a comprehensive literature review. Biomedicines, 10(11), 2697. [Google Scholar]
  22. Ringoringo, H. P. (2022). Prevalence of iron deficiency anemia and reference range of complete blood count, reticulocyte parameters in infants aged 9–11 months. International Journal of General Medicine, 15, 8017. [Google Scholar]
  23. Wisanuvej, K., Boonyawat, K., Savetamornkul, C., Virapongsiri, S., Krongvorakul, J., Sungkanuparph, S., & Phuphuakrat, A. (2021). Comparison between blood hemoglobin concentration determined by point-of-care device and complete blood count in adult pa-tients with dengue. PLoS neglected tropical diseases, 15(8), e0009692. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.