<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462021000300601</article-id>
<article-id pub-id-type="doi">10.13053/cys-25-3-3453</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Comparación de arquitecturas de redes neuronales convolucionales para el diagnóstico de COVID-19]]></article-title>
<article-title xml:lang="en"><![CDATA[Comparison of Convolutional Neural Network Architectures for COVID-19 Diagnosis]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Betancur]]></surname>
<given-names><![CDATA[Daniela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bosco Durán]]></surname>
<given-names><![CDATA[Rembrandt]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guerrero-Méndez]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zambrano Rodríguez]]></surname>
<given-names><![CDATA[Rogelia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Saucedo Anaya]]></surname>
<given-names><![CDATA[Tonatiuh]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Politécnica de Aguascalientes Dirección de Posgrado e Investigación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Física ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Ciencia y Tecnología de la Luz y la Materia ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Contaduría y Administración ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2021</year>
</pub-date>
<volume>25</volume>
<numero>3</numero>
<fpage>601</fpage>
<lpage>615</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462021000300601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462021000300601&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462021000300601&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Las redes neuronales convolucionales (CNNs, por sus siglas en inglés) han demostrado un gran potencial para resolver problemas de clasificación con imágenes médicas. En esta investigación, se evaluaron treinta y dos arquitecturas CNNs, y se compararon para realizar el diagnóstico COVID-19 mediante el uso de imágenes radiográficas. Se utilizó una colección de 5,953 imágenes de rayos X de tórax frontales (117 imágenes de pacientes diagnosticados con COVID-19, 4,273 de pacientes con neumonía no relacionada con COVID-19 y 1,563 imágenes etiquetadas como Normal provenientes de pacientes saludables) para entrenar y evaluar las arquitecturas. En este artículo, las métricas de evaluación implementadas están en concordancia con las condiciones requeridas para un conjunto de datos desequilibrado. Siete de los treinta y dos modelos evaluados lograron una clasificación de rendimiento excelente (&#8805;90%) según la métrica del Índice de precisión equilibrada (IBA, por sus siglas en inglés). Los tres modelos de CNNs que obtuvieron los mejores resultados en esta investigación fueron Wide_resnet101_2, Resnext101_32x8d y Resnext50_32x4d, los cuales obtuvieron un valor de precisión de clasificación del 97.75%. El problema de sobreajuste en los modelos se descartó de acuerdo con el comportamiento de los valores de precisión tanto en el conjunto de datos de entrenamiento, como en los de prueba. El mejor modelo para el diagnóstico de COVID-19 es el Resnext101_32x8d, de acuerdo con la matriz de confusión y las métricas logradas de sensibilidad, especificidad, F1-score, G_mean, IBA y tiempo de entrenamiento de 97.75%, 96.40%, 97.75%, 97.06%, 94.34%, 76.98 min, respectivamente.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Convolutional neural networks (CNNs) have shown great potential to solve several medical image classification problems. In this research, thirty-two CNN architectures were evaluated and compared to perform COVID-19 diagnosis by using radiographic images. A collection of 5,953 frontal chest X-ray images (117 patients diagnosed with COVID-19, 4,273 with Pneumonia not related to COVID-19, and 1,563 Normal or healthy) was used for training and testing those thirty-two architectures. In this article, the implemented metrics were according to the conditions of an imbalanced dataset. Seven of the thirty-two models evaluated achieved an excellent performance classification (&#8805;90%) according to the Index of Balanced Accuracy (IBA) metric. The top three CNN models selected in this research (Wide_resnet101_2, Resnext101_32x8d, and Resnext50_32x4d) obtained the highest classification precision value of 97.75%. The overfitting problem was ruled out according to the evolution of the training and testing precision measurement. The best CNN model for COVID-19 diagnosis is the Resnext101_32x8d according to the confusion matrix and the metrics achieved (sensitivity, specificity, F1-score, G_mean, IBA, and training time of 97.75%, 96.40%, 97.75%, 97.06%, 94.34%, 76.98 min, respectively) by the CNN model.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[transferencia de aprendizaje]]></kwd>
<kwd lng="en"><![CDATA[Convolutional neural network]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[Transfer learning]]></kwd>
</kwd-group>
</article-meta>
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