Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection

Introducción: La pandemia de COVID-19 ha impactado la salud pública y las economías mundiales. Las radiografías de tórax (RTC) desempeñan un papel fundamental en el cribado, especialmente en regiones con recursos limitados. Este trabajo se centra en el desarrollo de un modelo de aprendizaje profundo...

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Autores:: Escobar Ortiz, Andres F.
Pardo-Cabrera, Josh
Hurtado López, Julián
Ramírez Moreno, David Fernando
Amezquita-Dussan, María A.
Galindo-Sánchez, Juan S.
Sua-Villegas, Luz F.
Fernández-Trujillo, Liliana

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2024

Institución:: Universidad Autónoma de Occidente

Repositorio:: RED: Repositorio Educativo Digital UAO

Idioma:: eng

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network_acronym_str	REPOUAO2
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repository_id_str
dc.title.eng.fl_str_mv	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
dc.title.translated.spa.fl_str_mv	Balance de clases de conjuntos de datos y el transformador de visión convolucional: un análisis de radiografías de tórax para la detección del SARS-CoV-2
title	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
spellingShingle	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection COVID-19 Diagnóstico por imágenes Radiografía Tórax Redes neuronales Computadora Aprendizaje profundo Diagnostic imaging Radiography Thorax Neural networks Computer
title_short	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
title_full	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
title_fullStr	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
title_full_unstemmed	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
title_sort	Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection
dc.creator.fl_str_mv	Escobar Ortiz, Andres F. Pardo-Cabrera, Josh Hurtado López, Julián Ramírez Moreno, David Fernando Amezquita-Dussan, María A. Galindo-Sánchez, Juan S. Sua-Villegas, Luz F. Fernández-Trujillo, Liliana
dc.contributor.author.none.fl_str_mv	Escobar Ortiz, Andres F. Pardo-Cabrera, Josh Hurtado López, Julián Ramírez Moreno, David Fernando Amezquita-Dussan, María A. Galindo-Sánchez, Juan S. Sua-Villegas, Luz F. Fernández-Trujillo, Liliana
dc.subject.proposal.spa.fl_str_mv	COVID-19 Diagnóstico por imágenes Radiografía Tórax Redes neuronales Computadora Aprendizaje profundo
topic	COVID-19 Diagnóstico por imágenes Radiografía Tórax Redes neuronales Computadora Aprendizaje profundo Diagnostic imaging Radiography Thorax Neural networks Computer
dc.subject.proposal.eng.fl_str_mv	Diagnostic imaging Radiography Thorax Neural networks Computer
description	Introducción: La pandemia de COVID-19 ha impactado la salud pública y las economías mundiales. Las radiografías de tórax (RTC) desempeñan un papel fundamental en el cribado, especialmente en regiones con recursos limitados. Este trabajo se centra en el desarrollo de un modelo de aprendizaje profundo para la detección de COVID-19 en RTC, con el objetivo de abordar los desafíos asociados con la interpretación manual. Métodos: Nuestro enfoque integra Transformadores de Visión Convolucional con Redes Neuronales Convolucionales tradicionales para el análisis de radiografías de tórax . Utilizando un conjunto de datos de 5572 radiografías de tórax, que incluían diversos grados de COVID-19 y otros tipos de neumonía, el modelo se entrenó con múltiples configuraciones y se evaluó mediante diversas métricas. Cada Configuración de Conjunto de Datos (DSC) implicó 10 iteraciones de entrenamiento. Empleamos una técnica de visualización de la atención mejorada para identificar áreas clave en las radiografías. Resultados: Este trabajo presenta los cinco mejores modelos según los resultados de entrenamiento y validación en diferentes clases radiográficas. El rendimiento del modelo varió según la configuración del conjunto de datos, en particular al distinguir la COVID-19 de otros tipos de neumonía. DSC 6 resultó ser el más eficaz, con una sensibilidad del 97,1 % y una tasa de falsos negativos del 2,9 % durante el entrenamiento, y manteniendo un alto rendimiento durante la validación con una sensibilidad del 87,2 % y una tasa de falsos negativos del 12,8 %. Conclusión: Esta fusión de ingeniería y experiencia médica da como resultado una herramienta de detección eficiente. La crisis de la COVID-19 subraya la importancia de mejorar la preparación sanitaria. Nuestro modelo propuesto alcanza un promedio de recuperación, precisión y sensibilidad superior al 90 %, junto con una baja tasa de falsos negativos. Identifica la COVID-19 de forma fiable en diferentes grados de gravedad y la distingue eficazmente de otros tipos de neumonía, lo que consolida su utilidad como una herramienta robusta para la detección de la COVID-19
publishDate	2024
dc.date.issued.none.fl_str_mv	2024
dc.date.accessioned.none.fl_str_mv	2025-07-08T17:16:01Z
dc.date.available.none.fl_str_mv	2025-07-08T17:16:01Z
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.eng.fl_str_mv	Text
dc.type.driver.eng.fl_str_mv	info:eu-repo/semantics/article
dc.type.redcol.eng.fl_str_mv	http://purl.org/redcol/resource_type/ART
dc.type.version.eng.fl_str_mv	info:eu-repo/semantics/publishedVersion
format	http://purl.org/coar/resource_type/c_2df8fbb1
status_str	publishedVersion
dc.identifier.citation.eng.fl_str_mv	Escobar Ortiz, A. F.; Pardo-Cabrera, J.; Hurtado López, J.; Ramirez-Moreno, D. F.; Amezquita-Dussan, M. A.; Galindo-Sánchez, J. S.; Sua-Villegas, L. F. y Fernández-Trujillo, L. (2024). Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection. Biomedical Signal Processing and Control. Vol. 93. p.p. 1-13. https://doi.org/10.1016/j.bspc.2024.106190
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10614/16207
dc.identifier.doi.spa.fl_str_mv	https://doi.org/10.1016/j.bspc.2024.106190
dc.identifier.eissn.spa.fl_str_mv	17468108
dc.identifier.instname.spa.fl_str_mv	Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv	Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv	https://red.uao.edu.co/
identifier_str_mv	Escobar Ortiz, A. F.; Pardo-Cabrera, J.; Hurtado López, J.; Ramirez-Moreno, D. F.; Amezquita-Dussan, M. A.; Galindo-Sánchez, J. S.; Sua-Villegas, L. F. y Fernández-Trujillo, L. (2024). Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection. Biomedical Signal Processing and Control. Vol. 93. p.p. 1-13. https://doi.org/10.1016/j.bspc.2024.106190 17468108 Universidad Autónoma de Occidente Respositorio Educativo Digital UAO
url	https://hdl.handle.net/10614/16207 https://doi.org/10.1016/j.bspc.2024.106190 https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.citationendpage.spa.fl_str_mv	13
dc.relation.citationstartpage.spa.fl_str_mv	1
dc.relation.citationvolume.spa.fl_str_mv	93
dc.relation.ispartofjournal.eng.fl_str_mv	Biomedical Signal Processing and Control
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spelling	Escobar Ortiz, Andres F.Pardo-Cabrera, JoshHurtado López, Juliánvirtual::6098-1Ramírez Moreno, David Fernandovirtual::6099-1Amezquita-Dussan, María A.Galindo-Sánchez, Juan S.Sua-Villegas, Luz F.Fernández-Trujillo, Liliana2025-07-08T17:16:01Z2025-07-08T17:16:01Z2024Escobar Ortiz, A. F.; Pardo-Cabrera, J.; Hurtado López, J.; Ramirez-Moreno, D. F.; Amezquita-Dussan, M. A.; Galindo-Sánchez, J. S.; Sua-Villegas, L. F. y Fernández-Trujillo, L. (2024). Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection. Biomedical Signal Processing and Control. Vol. 93. p.p. 1-13. https://doi.org/10.1016/j.bspc.2024.106190https://hdl.handle.net/10614/16207https://doi.org/10.1016/j.bspc.2024.10619017468108Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Introducción: La pandemia de COVID-19 ha impactado la salud pública y las economías mundiales. Las radiografías de tórax (RTC) desempeñan un papel fundamental en el cribado, especialmente en regiones con recursos limitados. Este trabajo se centra en el desarrollo de un modelo de aprendizaje profundo para la detección de COVID-19 en RTC, con el objetivo de abordar los desafíos asociados con la interpretación manual. Métodos: Nuestro enfoque integra Transformadores de Visión Convolucional con Redes Neuronales Convolucionales tradicionales para el análisis de radiografías de tórax . Utilizando un conjunto de datos de 5572 radiografías de tórax, que incluían diversos grados de COVID-19 y otros tipos de neumonía, el modelo se entrenó con múltiples configuraciones y se evaluó mediante diversas métricas. Cada Configuración de Conjunto de Datos (DSC) implicó 10 iteraciones de entrenamiento. Empleamos una técnica de visualización de la atención mejorada para identificar áreas clave en las radiografías. Resultados: Este trabajo presenta los cinco mejores modelos según los resultados de entrenamiento y validación en diferentes clases radiográficas. El rendimiento del modelo varió según la configuración del conjunto de datos, en particular al distinguir la COVID-19 de otros tipos de neumonía. DSC 6 resultó ser el más eficaz, con una sensibilidad del 97,1 % y una tasa de falsos negativos del 2,9 % durante el entrenamiento, y manteniendo un alto rendimiento durante la validación con una sensibilidad del 87,2 % y una tasa de falsos negativos del 12,8 %. Conclusión: Esta fusión de ingeniería y experiencia médica da como resultado una herramienta de detección eficiente. La crisis de la COVID-19 subraya la importancia de mejorar la preparación sanitaria. Nuestro modelo propuesto alcanza un promedio de recuperación, precisión y sensibilidad superior al 90 %, junto con una baja tasa de falsos negativos. Identifica la COVID-19 de forma fiable en diferentes grados de gravedad y la distingue eficazmente de otros tipos de neumonía, lo que consolida su utilidad como una herramienta robusta para la detección de la COVID-19Introduction: The COVID-19 pandemic has had impact on global public health and economies. Chest X-rays (CXRs) play a critical role in screening, especially in resource-constrained regions. This work focuses on the development of a deep learning model for detecting COVID-19 in CXRs images, aiming to address the challenges associated with manual interpretation. Methods: Our approach integrates Convolutional Vision Transformers with traditional Convolutional Neural Networks for CXRs analysis. Using a dataset comprising 5572 CXRs, including various COVID-19 severities and other pneumonia types, the model underwent training across multiple configurations and evaluation using various metrics. Each Dataset Configuration (DSC) involved 10 training iterations. We employed an enhanced attention visualization technique to identify key areas in X-rays. Results: This work presents the top five models based on training and validation outcomes across different radiographic classes. The model’s performance varied with dataset configurations, particularly in distinguishing COVID-19 from other pneumonia types. DSC 6 emerged as the most effective, demonstrating 97.1% sensitivity and a 2.9% false negative rate during training, and maintaining high performance during validation with 87.2% sensitivity and a 12.8% false negative rate. Conclusion: This fusion of engineering and medical expertise yields an efficient screening tool. The COVID-19 crisis underscores the importance of enhanced healthcare preparedness. Our proposed model achieves over 90% average recall, accuracy, and sensitivity, coupled with a low false negative rate. It reliably identifies COVID-19 across different severities and effectively distinguishes it from other pneumonia types, establishing its utility as a robust COVID-19 detection tool13 páginasapplication/pdfengScienceDirectPaíses BajosDerechos reservados - Elsevier, 2024https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detectionBalance de clases de conjuntos de datos y el transformador de visión convolucional: un análisis de radiografías de tórax para la detección del SARS-CoV-2Artículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a8513193Biomedical Signal Processing and Control[1] S. 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Data-set class-balancing and the convolutional vision transformer: An analysis of chest radiographs for SARS-CoV-2 detection

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