<?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>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282023000100031</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Potencial de YOLO para detectar nódulos pulmonares en rayos x de tórax]]></article-title>
<article-title xml:lang="en"><![CDATA[Potential of YOLOv5 to detect lung nodules on chest x-rays]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz Corzo]]></surname>
<given-names><![CDATA[Hanlert]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Díaz]]></surname>
<given-names><![CDATA[Marlen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Cabrera]]></surname>
<given-names><![CDATA[José Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central Marta Abreu de las Villas  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>1</numero>
<fpage>31</fpage>
<lpage>46</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282023000100031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282023000100031&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282023000100031&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La radiografía de tórax es uno de los métodos más extendidos para la identificación de nódulos pulmonares. Sin embargo, son difíciles de interpretar por su bajo contraste y la cantidad de estructuras anatómicas que se superponen en la región torácica. Los sistemas de diagnóstico asistido por ordenador (CAD) incrementan la efectividad de los diagnósticos y reducen la carga laboral de los especialistas. Esta investigación propone un sistema CAD basado en inteligencia artificial, donde se emplean las radiografías de tórax para la detección de nódulos pulmonares. Se utilizó una red neuronal con empleo del método YOLO, sobre la que se aplicaron técnicas de transferencia de aprendizaje y tres estrategias de entrenamiento. Se crearon conjuntos de imágenes a partir de cuatro bases de datos internacionales. La red fue entrenada y validada, y para el mejor modelo obtenido se realizó una prueba externa, a partir de una quinta base de datos de alta dificultad, de diferente origen a las anteriores. El mejor modelo se obtuvo con el entrenamiento para el conjunto de imágenes segmentadas, incluyendo al área del mediastino, con una sensibilidad del 68% en la prueba externa, comparable con algunos resultados de estudios previos. Este enfoque presenta, además de su potencial, la ventaja de poder visualizar y filtrar la confianza del modelo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Chest radiography is one of the most widespread methods for the identification of pulmonary nodules. However, they are difficult to interpret due to their low contrast and the amount of overlapping anatomical structures in the thoracic region. Computer-aided diagnostic (CAD) systems increase the effectiveness of diagnoses and reduce the workload of specialists. This research proposes a CAD system based on artificial intelligence, where chest X-rays are used for the detection of pulmonary nodules. A network with a YOLO method was used on which transfer learning techniques and three training strategies were applied. Image sets were created from four international databases. The network was trained and validated, and an external test was carried out for the best model obtained, based on a fifth highly difficult database of a different origin than the previous ones. The best model was obtained with the training for the set of segmented images, including the mediastinal area, with a sensitivity of 68% in the external test, which is comparable to previus studies. The approach presents potential and also the advantage to visualize and filtering the model confidence.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[radiografía de tórax]]></kwd>
<kwd lng="es"><![CDATA[nódulo pulmonar]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[YOLO]]></kwd>
<kwd lng="en"><![CDATA[chest x-ray]]></kwd>
<kwd lng="en"><![CDATA[lung nodule]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[YOLO]]></kwd>
</kwd-group>
</article-meta>
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