Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Narváez Semanate, José LuisMena Quintero, María Camila2022-02-212022-02-212022-01-27http://repositorio.uan.edu.co/handle/123456789/5972n hematology, the hemogram is one of the evaluative tests used with greater regularity in medical practice, since it allows to evaluate and quantify the different types of cells present in the blood. However, not all characteristics of blood cells can be detailed with this test, which is why a microscopic inspection of the peripheral blood smear is required. The manual exploration of the blood smear, allows to extract, among others, qualitative information about the blood cells, by means of a visual inspection with the help of the microscope; The inspection is a detailed and orderly process, which is carried out with the aim of looking for morphological changes that make it possible to establish differences between normality and abnormality. Since it is carried out manually, the results of this type of classification, based on qualitative parameters; they depend on the skill and experience of the evaluator, which can lead to mistakes, time and money. Taking into account the aforementioned, an erythrocyte classification method was implemented in Matlab, based on morphological descriptors (diameter, perimeter, area, solidity, circularity and concavity), from which a neural network was trained, from which a percentage of accuracy of 83.3% is obtained.En hematología, el hemograma es una de las pruebas valorativas empleadas con mayor regularidad en la praxis médica, ya que permite evaluar y cuantificar los diferentes tipos de células presentes en la sangre. Sin embargo, no todas las características de las células sanguíneas pueden detallarse con esta prueba, razón por la cual, se requiere realizar una inspección microscópica del extendido de sangre periférica. La exploración manual del frotis de sangre, permite extraer entre otros, información cualitativa acerca de las células sanguíneas, por medio de una inspección visual con ayuda del microscopio; la inspección es un proceso detallado y ordenado, que se realiza con el objetivo de buscar cambios morfológicos que permitan establecer diferencias entre normalidad y anormalidad. Dado que se realiza de manera manual, los resultados de este tipo de clasificación, basada en parámetros cualitativos; dependen de la habilidad y experiencia del evaluador, lo que puede implicar errores, gasto de tiempo y dinero. Teniendo en cuenta lo mencionado, se implementó en Matlab un método de clasificación eritrocitaria, basado en descriptores morfológicos (diámetro, perímetro, área, solidez, circularidad y concavidad), a partir de los cuales se entrenó una red neuronal, a partir de la cual se obtiene un porcentaje de exactitud del 83.3%.spaAcceso abiertoMétodo de clasificaciónred neuronalDeep Learningclasificación morfológicaTrabajo de grado (Pregrado y/o Especialización)Classification methoderythrocytesmorphological classificationDeep Learningneural networkinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Abdollahi, A., Saffar, H., & Saffar, H. (2014). Types and frequency of errors during different phases of testing at a clinical medical laboratory of a teaching hospital in Tehran, Iran. North American Journal of Medical Sciences, 6(5), 224–228. https://doi.org/10.4103/1947-2714.132941Acharya, V., & Kumar, P. (2017). Identification and red blood cell classification using computer aided system to diagnose blood disorders. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017-Janua, 2098–2104. https://doi.org/10.1109/ICACCI.2017.8126155Adewoyin, A. S., & Nwogoh, B. (2014). Peripheral blood film: A review. In Annals of Ibadan postgraduate medicine (Vol. 12, Issue 2, pp. 71–79). http://www.ncbi.nlm.nih.gov/pubmed/25960697%0Ahttp://www.pubmedcentral.nih.gov/articlerender.f cgi?artid=PMC4415389Adollah, R., Mashor, M. Y., Nasir, N. F. M., Rosline, H., Mahsin, H., & Adilah, H. (2008). Blood cell image segmentation : A review (pp. 141–144).Albertini, M. C., Teodori, L., Piatti, E., Piacentini, M. P., Accorsi, A., & Rocchi, M. B. L. (2003). Automated analysis of morphometric parameters for accurate definition of erythrocyte cell shape. Cytometry Part A, 52(1), 12–18. https://doi.org/10.1002/cyto.a.10019Aliyu, H. A., Sudirman, R., Abdul Razak, M. A., & Abd Wahab, M. A. (2018). Red blood cell classification: Deep learning architecture versus support vector machine. 2nd International Conference on BioSignal Analysis, Processing and Systems, ICBAPS 2018, February 2019, 142–147. https://doi.org/10.1109/ICBAPS.2018.8527398Almezhghwi, K., & Serte, S. (2020). Improved classification of white blood cells with the generative adversarial network and Deep convolutional neural network. Computational Intelligence and Neuroscience, 2020. https://doi.org/10.1155/2020/6490479Alzate, M. (2016). rojos en frotis de sangre periférica Automatic classification of red cells in peripheral blood smears. 48(3), 311–319.Arquitectura, E. Y., Introducci, T. I., 赫晓霞, Iv, T., Teatinas, L. A. S., Conclusiones, T. V. I. I., Contemporáneo, P. D. E. U. S. O., Evaluaci, T. V, Ai, F., Jakubiec, J. A., Weeks, D. P. C. C. L. E. Y. N. to K. in 20, Mu, A., Inan, T., Sierra Garriga, C., Library, P. Y., Hom, H., Kong, H., Castilla, N., Uzaimi, A., … Bain, B. J. (2016). Khan’s the physics of radiation therapy, 5th edition. Medisur, 15(1), 183–192. https://doi.org/10.4103/2153-3539.129442Arul, P., Pushparaj, M., Pandian, K., Chennimalai, L., Rajendran, K., Selvaraj, E., & Masilamani, S. (2018). Prevalence and types of preanalytical error in hematology laboratory of a tertiary care hospital in South India. Journal of Laboratory Physicians, 10(02), 237–240. https://doi.org/10.4103/jlp.jlp_98_17 ASH. (1958). American Society of Hematology. https://www.hematology.org/educationinstname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/