Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning
n 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 micro...
- Autores:
-
Mena Quintero, María Camila
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad Antonio Nariño
- Repositorio:
- Repositorio UAN
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uan.edu.co:123456789/5972
- Acceso en línea:
- http://repositorio.uan.edu.co/handle/123456789/5972
- Palabra clave:
- Método de clasificación
red neuronal
Deep Learning
clasificación morfológica
Classification method
erythrocytes
morphological classification
Deep Learning
neural network
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| title |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| spellingShingle |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning Método de clasificación red neuronal Deep Learning clasificación morfológica Classification method erythrocytes morphological classification Deep Learning neural network |
| title_short |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| title_full |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| title_fullStr |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| title_full_unstemmed |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| title_sort |
Clasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learning |
| dc.creator.fl_str_mv |
Mena Quintero, María Camila |
| dc.contributor.advisor.spa.fl_str_mv |
Narváez Semanate, José Luis |
| dc.contributor.author.spa.fl_str_mv |
Mena Quintero, María Camila |
| dc.subject.es_ES.fl_str_mv |
Método de clasificación red neuronal Deep Learning clasificación morfológica |
| topic |
Método de clasificación red neuronal Deep Learning clasificación morfológica Classification method erythrocytes morphological classification Deep Learning neural network |
| dc.subject.keyword.es_ES.fl_str_mv |
Classification method erythrocytes morphological classification Deep Learning neural network |
| description |
n 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. |
| publishDate |
2022 |
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2022-02-21T20:52:36Z |
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2022-02-21T20:52:36Z |
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2022-01-27 |
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Trabajo de grado (Pregrado y/o Especialización) |
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Abdollahi, 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/10.4103/1947-2714.132941 Acharya, 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.2017.8126155 Adewoyin, 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=PMC4415389 Adollah, 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.10019 Aliyu, 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.2018.8527398 Almezhghwi, 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/10.1155/2020/6490479 Alzate, 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/10.4103/2153-3539.129442 Arul, 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/education |
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Abdollahi, 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/10.4103/1947-2714.132941 Acharya, 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.2017.8126155 Adewoyin, 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=PMC4415389 Adollah, 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.10019 Aliyu, 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.2018.8527398 Almezhghwi, 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/10.1155/2020/6490479 Alzate, 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/10.4103/2153-3539.129442 Arul, 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/education instname:Universidad Antonio Nariño reponame:Repositorio Institucional UAN repourl:https://repositorio.uan.edu.co/ |
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Facultad de Ingeniería Mecánica, Electrónica y Biomédica |
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Popayán - Alto Cauca |
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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/n 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%.Ingeniero(a) Biomédico(a)PregradoPresencialMonografíaspaUniversidad Antonio NariñoIngeniería BiomédicaFacultad de Ingeniería Mecánica, Electrónica y BiomédicaPopayán - Alto CaucaMétodo de clasificaciónred neuronalDeep Learningclasificación morfológicaClassification methoderythrocytesmorphological classificationDeep Learningneural networkClasificación morfológica de eritrocitos en imágenes digitales de frotis de sangre periférica mediante deep learningTrabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85GeneralORIGINAL2021_MariaMena2021_MariaMenaapplication/pdf2008121https://repositorio.uan.edu.co/bitstreams/04558592-c8ab-45bd-8fc5-3692854e6574/downloadd926d2dad96e652ceee8b358f92cb5b2MD532021_MariaMena_Acta2021_MariaMena_Actaapplication/pdf149488https://repositorio.uan.edu.co/bitstreams/400646c9-f5c4-4f2f-8444-17a1f5040645/downloadc1eb3da7bb226b3368c022f8778e801bMD522021_MariaMena_Autorización2021_MariaMena_Autorizaciónapplication/pdf1952383https://repositorio.uan.edu.co/bitstreams/5703dedd-4f5f-4a2f-9afe-57dacc711232/download3abc257c280ace4c11bb369f390e71f1MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.uan.edu.co/bitstreams/d6382fe2-91cb-410a-a27c-5ce320780679/download9868ccc48a14c8d591352b6eaf7f6239MD54123456789/5972oai:repositorio.uan.edu.co:123456789/59722024-10-09 22:48:36.943https://creativecommons.org/licenses/by-nc-nd/4.0/Acceso abiertoopen.accesshttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co |