<?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>1684-1859</journal-id>
<journal-title><![CDATA[Revista Cubana de Informática Médica]]></journal-title>
<abbrev-journal-title><![CDATA[RCIM]]></abbrev-journal-title>
<issn>1684-1859</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de La Habana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18592022000100005</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Clasificación de Imágenes de Neumonía a causa de Covid-19 utilizando Transfer-Learning basado en Redes Convolucionales]]></article-title>
<article-title xml:lang="en"><![CDATA[Classification of Images of Pneumonia Due to Covid-19 Using Transfer Learning, Based on Convolutional Networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Preciado Rodríguez]]></surname>
<given-names><![CDATA[Adiel Joshua]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Flores Guillen]]></surname>
<given-names><![CDATA[Flor Mayerli]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Soraluz Soraluz]]></surname>
<given-names><![CDATA[Aldo Emanuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ríos Jara]]></surname>
<given-names><![CDATA[Jonathan Gerhard]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Peruana Unión  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>14</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18592022000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18592022000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18592022000100005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La Inteligencia Artificial ha ayudado a lidiar diferentes problemas relacionados con los datos masivos y a su vez con su tratamiento, diagnóstico y detección de enfermedades como la que actualmente nos preocupa, la Covid-19. El objetivo de esta investigación ha sido analizar y desarrollar la clasificación de imágenes de neumonía a causa de covid-19 para un diagnostico efectivo y óptimo. Se ha usado Transfer-Learning aplicando ResNet, DenseNet, Poling y Dense layer para la elaboración de los modelos de red propios Covid-UPeU y Covid-UPeU-TL, utilizando las plataformas Kaggle y Google colab, donde se realizaron 4 experimentos. El resultado con una mejor clasificación de imágenes se obtuvo en el experimento 4 prueba N°2 con el modelo Covid-UPeU-TL donde Acc.Train: 0.9664 y Acc.Test: 0.9851. Los modelos implementados han sido desarrollados con el propósito de tener una visión holística de los factores para la optimización en la clasificación de imágenes de neumonía a causa de COVID-19.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Artificial Intelligence has helped to deal with different problems related to massive data in turn to the treatment, diagnosis and detection of diseases such as the one that currently has us in concern, Covid-19. The objective of this research has been to analyze and develop the classification of images of pneumonia due to covid-19 for an effective and optimal diagnosis. Transfer-Learning has been used applying ResNet, DenseNet, Poling and Dense layer for the elaboration of the own network models Covid-Upeu and Covid-UpeU-TL, using Kaggle and Google colab platforms, where 4 experiments have been carried out. The result with a better classification of images was obtained in experiment 4 test N ° 2 with the Covid-UPeU-TL model where Acc.Train: 0.9664 and Acc.Test: 0.9851. The implemented models have been developed with the purpose of having a holistic view of the factors for optimization in the classification of COVID-19 images.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[covid-19]]></kwd>
<kwd lng="es"><![CDATA[transfer-learning]]></kwd>
<kwd lng="es"><![CDATA[reconocimiento]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[pandemia]]></kwd>
<kwd lng="es"><![CDATA[rayos x]]></kwd>
<kwd lng="es"><![CDATA[clasificación de imágenes]]></kwd>
<kwd lng="es"><![CDATA[pulmones]]></kwd>
<kwd lng="es"><![CDATA[redes convolucionales]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[Transfer-Learnig]]></kwd>
<kwd lng="en"><![CDATA[Recognition]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Pandemic]]></kwd>
<kwd lng="en"><![CDATA[X-rays]]></kwd>
<kwd lng="en"><![CDATA[Image classification]]></kwd>
<kwd lng="en"><![CDATA[Lungs]]></kwd>
<kwd lng="en"><![CDATA[convolutional networks]]></kwd>
</kwd-group>
</article-meta>
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