<?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>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322022000100100</article-id>
<article-id pub-id-type="doi">10.17488/rmib.43.1.1</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Detection of COVID-19 Lung Lesions in Computed Tomography Images Using Deep Learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Detección de lesiones pulmonares por COVID-19 en imágenes de tomografía computarizada mediante aprendizaje profundo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arreola Minjarez]]></surname>
<given-names><![CDATA[Joy Ingrid]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz Román]]></surname>
<given-names><![CDATA[José David]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mederos Madrazo]]></surname>
<given-names><![CDATA[Boris Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mejía Muñoz]]></surname>
<given-names><![CDATA[José Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rascón Madrigal]]></surname>
<given-names><![CDATA[Lidia Hortencia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cota Ruiz]]></surname>
<given-names><![CDATA[Juan de Dios]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<volume>43</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322022000100100&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322022000100100&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322022000100100&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The novel coronavirus (COVID-19) is a disease that mainly affects the lung tissue. The detection of lesions caused by this disease can help to provide an adequate treatment and monitoring its evolution. This research focuses on the bi- nary classification of lung lesions caused by COVID-19 in images of computed tomography (CT) using deep learning. The database used in the experiments comes from two independent repositories, which contains tomographic scans of patients with a positive diagnosis of COVID-19. The output layers of four pre-trained convolutional networks were adapted to the proposed task and re-trained using the fine-tuning technique. The models were validated with test images from the two database&#8217;s repositories. The model VGG19, considering one of the repositories, showed the best performance with 88% and 90.2% of accuracy and recall, respectively. The model combination using the soft voting technique presented the highest accuracy (84.4%), with a recall of 94.4% employing the data from the other repository. The area under the receiver operating characteristic curve was 0.92 at best. The proposed method based on deep learning represents a valuable tool to automatically classify COVID-19 lesions on CT images and could also be used to assess the extent of lung infection.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Lung Lesions]]></kwd>
<kwd lng="en"><![CDATA[Classification]]></kwd>
<kwd lng="en"><![CDATA[Deep Learning]]></kwd>
<kwd lng="en"><![CDATA[Computed Tomography]]></kwd>
</kwd-group>
</article-meta>
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