<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462022000401491</article-id>
<article-id pub-id-type="doi">10.13053/cys-26-4-4090</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Big Medical Image Analysis: Alzheimer&#8217;s Disease Classification Using Convolutional Autoencoder]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mansingh]]></surname>
<given-names><![CDATA[Padmini]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pattanayak]]></surname>
<given-names><![CDATA[Binod Kumar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pati]]></surname>
<given-names><![CDATA[Bibudhendu]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Deemed to be University Department of Computer Science and Engineering Institute of Technical Education and Research Siksha 'O' Anusandhan]]></institution>
<addr-line><![CDATA[Bhubaneswar Odisha]]></addr-line>
<country>India</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Ramadevi Women's University Department of Computer Science ]]></institution>
<addr-line><![CDATA[Bhubaneswar Odisha]]></addr-line>
<country>India</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>26</volume>
<numero>4</numero>
<fpage>1491</fpage>
<lpage>1501</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462022000401491&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462022000401491&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462022000401491&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Deep learning-based analysis is a noticeable topic in recent years. The enormous success of deep learning is now combined with big data analytics to provide an open platform to the healthcare industry for a better diagnosis of any disease. In this paper, we described the convolutional autoencoder technique that reduces the complexity of radiologists through a brief study of Alzheimer's MRI data, which led to a rise in data-driven medical research for a better diagnosis. In this research, we have compared the effects of two techniques: convolutional autoencoder (CANN) and independent component analysis (ICA), and discovered that CANN has a higher accuracy of 99.42% and outperforms ICA models in terms of convergence speed.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Deep learning]]></kwd>
<kwd lng="en"><![CDATA[big data analytics]]></kwd>
<kwd lng="en"><![CDATA[CANN]]></kwd>
<kwd lng="en"><![CDATA[ICA]]></kwd>
<kwd lng="en"><![CDATA[healthcare]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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