<?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>S0188-95322021000300101</article-id>
<article-id pub-id-type="doi">10.17488/rmib.42.3.2</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Image-based Glaucoma Classification Using Fundus Images and Deep Learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Clasificación de Glaucoma Basada en Imágenes de Fondo de Ojo y Aprendizaje Profundo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sandoval-Cuellar]]></surname>
<given-names><![CDATA[H. J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alfonso-Francia]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez-Membrillo]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramos-Arreguín]]></surname>
<given-names><![CDATA[J. M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tovar-Arriaga]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Querétaro  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Mexicano de Oftalmología  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>42</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322021000300101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322021000300101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322021000300101&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Glaucoma is an eye disease that gradually affects the optic nerve. Intravascular high pressure can be controlled to prevent total vision loss, but early glaucoma detection is crucial. The optic disc has been a notable landmark for finding abnormalities in the retina. The rapid development of computer vision techniques has made it possible to analyze eye conditions from images enabling to help a specialist to make a diagnosis using a technique that is non-invasive in its initial stage through fundus images. We propose a methodology glaucoma detection using deep learning. A convolutional neural network (CNN) is trained to extract multiple features, to classify fundus images. The accuracy, sensitivity, and the area under the curve obtained using the ORIGA database are 93.22%, 94.14%, and 93.98%. The use of the algorithm for the automatic region of interest detection in conjunction with our CNN structure considerably increases the glaucoma detecting accuracy in the ORIGA database.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El glaucoma es una enfermedad que afecta gradualmente al nervio óptico. La presión intravascular se puede controlar para prevenir la pérdida de visión, por lo que la detección temprana del glaucoma es crucial. El disco óptico ha sido un punto de referencia importante para encontrar anormalidades en la retina. El rápido desarrollo de técnicas de visión por computadora ha hecho posible el analizar las condiciones del ojo ayudando al especialista a realizar un diagnóstico utilizando una técnica no invasiva en su estadio inicial en imágenes de fondo de ojo. En este artículo, se propone una arquitectura para la detección de glaucoma utilizando aprendizaje profundo. Una red neuronal convolucional (RNC) es entrenada para extraer múltiples características, para clasificar imágenes de fondo de ojo. La exactitud, sensibilidad, y el área bajo la curva obtenidos en la base de datos ORIGA son 93.22%, 94.14% y 93.98%. El uso del algoritmo para la detección automática de la región de interés, incrementa considerablemente la exactitud de detección de glaucoma en la base de datos ORIGA.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Deep Learning]]></kwd>
<kwd lng="en"><![CDATA[Glaucoma diagnosis]]></kwd>
<kwd lng="en"><![CDATA[Image-based classification]]></kwd>
<kwd lng="en"><![CDATA[Convolutional Neural Network]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[Diagnóstico del glaucoma]]></kwd>
<kwd lng="es"><![CDATA[Clasificación de imágenes]]></kwd>
<kwd lng="es"><![CDATA[Red neuronal convolucional]]></kwd>
</kwd-group>
</article-meta>
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