<?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>0123-3033</journal-id>
<journal-title><![CDATA[Ingeniería y competitividad]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. compet.]]></abbrev-journal-title>
<issn>0123-3033</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad del Valle]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-30332024000100005</article-id>
<article-id pub-id-type="doi">10.25100/iyc.v26i1.13181</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Morphological Segmentation and Level Classification for Diabetic and Hypertensive Retinopathy through Ophthalmic Images and Convolutional Networks]]></article-title>
<article-title xml:lang="es"><![CDATA[Segmentación morfológica y clasificación de niveles para la retinopatía diabética e hipertensiva mediante imágenes oftálmicas y redes convolucionales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Niño]]></surname>
<given-names><![CDATA[María C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez]]></surname>
<given-names><![CDATA[Darwin E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Sergio Arboleda Escuela de Ciencias Exactas e Ingeniería ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<volume>26</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-30332024000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-30332024000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-30332024000100005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The detection and diagnosis of diabetes and hypertension present a challenging scenario, mainly in the early stages. These two are degenerative and with severe consequences, causing damage to multiple organs, including the eyes. The ophthalmologist examines the retinal fundus to discriminate between the type of disease. In the case of retinopathy, as a chronic disease, evidence in various classes of progression. Diabetic retinopathy is categorized into four, which include mild, moderate, severe, and proliferative stages. In the proliferative stage, the retina produces vasoproliferative factors that stimulate the growth of new blood vessels, increasing the risk of vision loss. As for the hypertensive retinopathy the most widely used method for classifying is the Keith-Wagener-Barker classification. Like diabetic retinopathy, it consists also of four classes, ranging from mild to severe. In the severe stage, there is an increased risk of developing papilledema or retinal edema. The main objective of this research is to perform segmentation and classification of diabetes and hypertensive retinopathy retinal fundus images. Using a combination of a convolutional network UNet and a ConvNet was proposed for vessel mask segmentation and retinopathy classification, respectively. The classification process relies on ten defined classes, where values ranging from 0 to 4 represent diabetic retinopathy, and values ranging from 5 to 9 correspond to hypertensive retinopathy. The approximate results in the segmentation were Jaccard index of 74%, F1 of 85%, and an Accuracy of 96%, and in the classification was Accuracy of 80%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La detección y el diagnóstico de diabetes e hipertensión presentan un escenario desafiante, principalmente en etapas tempranas. Estos dos son degenerativos y de graves consecuencias, provocando daños en múltiples órganos, incluidos los ojos. El oftalmólogo examina el fondo de la retina para discriminar el tipo de enfermedad. En el caso de la retinopatía, como enfermedad crónica, se evidencia en varias clases de progresión. La retinopatía diabética se clasifica en cuatro, que incluyen etapas leve, moderada, grave y proliferativa. En la etapa proliferativa, la retina produce factores vasoproliferativos que estimulan el crecimiento de nuevos vasos sanguíneos, aumentando el riesgo de pérdida de visión. En cuanto a la retinopatía hipertensiva, el método de clasificación más utilizado es la clasificación de Keith-Wagener-Barker. Similar a la retinopatía diabética, también consta de cuatro clases, que van de leves a graves. En la etapa grave, existe un mayor riesgo de desarrollar papiledema o edema retiniano. El objetivo principal de esta investigación es realizar la segmentación y clasificación de imágenes de fondo de retina con retinopatía diabética e hipertensiva. Se propuso una combinación de una red convolucional UNet y una ConvNet para la segmentación de máscara de vasos y la clasificación de retinopatía, respectivamente. El proceso de clasificación se basa en diez clases definidas, donde los valores que van del 0 al 4 representan la retinopatía diabética y los valores del 5 al 9 corresponden a la retinopatía hipertensiva. Los resultados aproximados en la segmentación fueron índices Jaccard de 74%, F1 de 85% y un Accuracy de 96%, y en la clasificación un Accuracy de 80%.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Diabetic and hypertensive retinopathy]]></kwd>
<kwd lng="en"><![CDATA[Computer vision]]></kwd>
<kwd lng="en"><![CDATA[Convolutional Neural Networks]]></kwd>
<kwd lng="es"><![CDATA[Retinopatía diabética e hipertensiva]]></kwd>
<kwd lng="es"><![CDATA[Visión por computador]]></kwd>
<kwd lng="es"><![CDATA[Redes Neuronales Convolucionales]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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