<?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>1665-6423</journal-id>
<journal-title><![CDATA[Journal of applied research and technology]]></journal-title>
<abbrev-journal-title><![CDATA[J. appl. res. technol]]></abbrev-journal-title>
<issn>1665-6423</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-64232024000200180</article-id>
<article-id pub-id-type="doi">10.22201/icat.24486736e.2024.22.2.2240</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[The role of convolution neural networks in detecting cancer]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hatem]]></surname>
<given-names><![CDATA[M. Q.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Middle Technical University Technical Institute of Baqubah Renewable Energy Department]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Iraq</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>22</volume>
<numero>2</numero>
<fpage>180</fpage>
<lpage>188</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-64232024000200180&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-64232024000200180&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-64232024000200180&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Breast cancer has the second highest death rate among women, making it one of the most alarming cancers in terms of lethality. The number of breast cancer patients is predicted to substantially rise in the next years. Because early detection of cancer in general and breast cancer in particular, may save many lives, this stage must be completed precisely and without delay. As a result, developing an automated model to assist pathologists in correctly recognizing breast cancers and categorizing them as benign or malignant is critical. Convolutional neural networks (CNNs) have played a significant role in detecting cancer through medical imaging, where CNNs excel at automatically extracting relevant features from medical images, such as X-rays, CT scans, and MRIs, making it easier to identify subtle abnormalities, this makes it outperforming traditional methods due to their ability to learn complex patterns. In this paper, we present a model that uses a convolutional neural network (CNN) to effectively categorize breast cancers as benign or malignant based on histological findings. The suggested methodology is simple to use, provides quick results, and ensures precise breast cancer detection. The proposed model was evaluated using MATLAB and the proposed model achieved 0.15066 loss value and its accuracy value is 0.84934, according to the experimental data.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Breast cancer]]></kwd>
<kwd lng="en"><![CDATA[CNN]]></kwd>
<kwd lng="en"><![CDATA[SVM]]></kwd>
<kwd lng="en"><![CDATA[loss]]></kwd>
<kwd lng="en"><![CDATA[accuracy]]></kwd>
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