<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-03125885</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-16T08:55:31+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">Transfer Learning Using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images</title>
            <author role="aut">
              <persName>
                <forename type="first">Rayene</forename>
                <surname>Chelghoum</surname>
              </persName>
              <idno type="halauthorid">2140903-0</idno>
              <affiliation ref="#struct-240669"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Ameur</forename>
                <surname>Ikhlef</surname>
              </persName>
              <idno type="halauthorid">2140904-0</idno>
              <affiliation ref="#struct-240669"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Amina</forename>
                <surname>Hameurlaine</surname>
              </persName>
              <idno type="halauthorid">1444805-0</idno>
              <affiliation ref="#struct-240669"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Sabir</forename>
                <surname>Jacquir</surname>
              </persName>
              <email type="md5">83d862e6da1469be31b42b079f8b38ea</email>
              <email type="domain">u-psud.fr</email>
              <idno type="idhal" notation="string">sabir-jacquir</idno>
              <idno type="idhal" notation="numeric">742544</idno>
              <idno type="halauthorid" notation="string">8321-742544</idno>
              <idno type="ORCID">https://orcid.org/0000-0002-6296-7888</idno>
              <idno type="IDREF">https://www.idref.fr/111488729</idno>
              <affiliation ref="#struct-1051126"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Sabir</forename>
                <surname>Jacquir</surname>
              </persName>
              <email type="md5">83d862e6da1469be31b42b079f8b38ea</email>
              <email type="domain">u-psud.fr</email>
            </editor>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2021-01-29 19:05:38</date>
              <date type="whenModified">2026-01-20 13:58:03</date>
              <date type="whenReleased">2021-01-29 19:05:38</date>
              <date type="whenProduced">2020-06-05</date>
              <ref type="externalLink" target="https://link.springer.com/content/pdf/10.1007%2F978-3-030-49161-1_17.pdf"/>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="115760">
                <persName>
                  <forename>Sabir</forename>
                  <surname>Jacquir</surname>
                </persName>
                <email type="md5">83d862e6da1469be31b42b079f8b38ea</email>
                <email type="domain">u-psud.fr</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-03125885</idno>
            <idno type="halUri">https://hal.science/hal-03125885</idno>
            <idno type="halBibtex">chelghoum:hal-03125885</idno>
            <idno type="halRefHtml">&lt;i&gt;16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)&lt;/i&gt;, Jun 2020, Neos Marmaras, Greece. pp.189-200, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-030-49161-1_17"&gt;&amp;#x27E8;10.1007/978-3-030-49161-1_17&amp;#x27E9;&lt;/a&gt;</idno>
            <idno type="halRef">16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.189-200, &amp;#x27E8;10.1007/978-3-030-49161-1_17&amp;#x27E9;</idno>
            <availability status="restricted"/>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
            <idno type="stamp" n="IFIP">IFIP - International Federation for Information Processing</idno>
            <idno type="stamp" n="IFIP-AICT" corresp="IFIP">IFIP Advances in Information and Communication Technology</idno>
            <idno type="stamp" n="IFIP-TC" corresp="IFIP">IFIP Technical Committees </idno>
            <idno type="stamp" n="IFIP-WG" corresp="IFIP">Working Groups</idno>
            <idno type="stamp" n="IFIP-TC12" corresp="IFIP-TC">TC12 - Artificial Intelligence</idno>
            <idno type="stamp" n="IFIP-AIAI">IFIP-AIAI</idno>
            <idno type="stamp" n="IFIP-WG12-5" corresp="IFIP-WG">IFIP-WG12-5</idno>
            <idno type="stamp" n="UNIV-PARIS-SACLAY">Université Paris-Saclay</idno>
            <idno type="stamp" n="TEST-HALCNRS">Collection test HAL CNRS</idno>
            <idno type="stamp" n="UNIVERSITE-PARIS-SACLAY" corresp="UNIV-PARIS-SACLAY">Université Paris-Saclay</idno>
            <idno type="stamp" n="GS-LIFE-SCIENCES-HEALTH" corresp="UNIVERSITE-PARIS-SACLAY">Graduate School Life Sciences and Health</idno>
            <idno type="stamp" n="GS-HEALTH-DRUG-SCIENCES">Graduate School Health and Drug Sciences</idno>
            <idno type="stamp" n="IFIP-AICT-583" corresp="IFIP-AICT">Artificial Intelligence Applications and Innovations</idno>
            <idno type="stamp" n="NEURO-PSI">Institut des neurosciences Paris-Saclay</idno>
          </seriesStmt>
          <notesStmt>
            <note type="commentary">Part 3: Image processing</note>
            <note type="audience" n="2">International</note>
            <note type="invited" n="0">No</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
            <note type="proceedings" n="0">No</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">Transfer Learning Using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Rayene</forename>
                    <surname>Chelghoum</surname>
                  </persName>
                  <idno type="halauthorid">2140903-0</idno>
                  <affiliation ref="#struct-240669"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Ameur</forename>
                    <surname>Ikhlef</surname>
                  </persName>
                  <idno type="halauthorid">2140904-0</idno>
                  <affiliation ref="#struct-240669"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Amina</forename>
                    <surname>Hameurlaine</surname>
                  </persName>
                  <idno type="halauthorid">1444805-0</idno>
                  <affiliation ref="#struct-240669"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Sabir</forename>
                    <surname>Jacquir</surname>
                  </persName>
                  <email type="md5">83d862e6da1469be31b42b079f8b38ea</email>
                  <email type="domain">u-psud.fr</email>
                  <idno type="idhal" notation="string">sabir-jacquir</idno>
                  <idno type="idhal" notation="numeric">742544</idno>
                  <idno type="halauthorid" notation="string">8321-742544</idno>
                  <idno type="ORCID">https://orcid.org/0000-0002-6296-7888</idno>
                  <idno type="IDREF">https://www.idref.fr/111488729</idno>
                  <affiliation ref="#struct-1051126"/>
                </author>
              </analytic>
              <monogr>
                <title level="m">Artificial Intelligence Applications and Innovations</title>
                <meeting>
                  <title>16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)</title>
                  <date type="start">2020-06-05</date>
                  <date type="end">2020-06-07</date>
                  <settlement>Neos Marmaras</settlement>
                  <country key="GR">Greece</country>
                </meeting>
                <editor>Ilias Maglogiannis</editor>
                <editor>Lazaros Iliadis</editor>
                <editor>Elias Pimenidis</editor>
                <imprint>
                  <publisher>Springer International Publishing</publisher>
                  <biblScope unit="volume">AICT-583</biblScope>
                  <biblScope unit="pp">189-200</biblScope>
                  <date type="datePub">2020-05-29</date>
                </imprint>
              </monogr>
              <idno type="doi">10.1007/978-3-030-49161-1_17</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">Transfer learning</term>
                <term xml:lang="en">Magnetic resonance images</term>
                <term xml:lang="en">Deep learning</term>
                <term xml:lang="en">Classification</term>
                <term xml:lang="en">Brain tumor</term>
                <term xml:lang="en">Convolutional Neural Network</term>
              </keywords>
              <classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
              <classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
              <classCode scheme="halDomain" n="sdv">Life Sciences [q-bio]</classCode>
              <classCode scheme="halTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halOldTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halTreeTypology" n="COMM">Conference papers</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Brain tumor classification is very important in medical applications todevelop an effective treatment. In this paper, we use brain contrast-enhanced magneticresonance images (CE-MRI) benchmark dataset to classify three types of brain tumor(glioma, meningioma and pituitary). Due to the small number of training dataset, ourclassification systems evaluate deep transfer learning for feature extraction using ninedeep pre-trained convolutional Neural Networks (CNNs) architectures. The objectiveof this study is to increase the classification accuracy, speed the training time andavoid the overfitting. In this work, we trained our architectures involved minimal pre-processing for three different epoch number in order to study its impact onclassification performance and consuming time. In addition, the paper benefitsacceptable results with small number of epoch in limited time. Our interpretationsconfirm that transfer learning provides reliable results in the case of small dataset. Theproposed system outperforms the state-of-the-art methods and achieve 98.71%classification accuracy</p>
            </abstract>
            <particDesc>
              <org type="consortium">TC 12</org>
              <org type="consortium">WG 12.5</org>
            </particDesc>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="laboratory" xml:id="struct-240669" status="VALID">
          <orgName>Laboraroire d'Automatique et de Robotique</orgName>
          <orgName type="acronym">LARC</orgName>
          <desc>
            <address>
              <addrLine>Université Constantine 1</addrLine>
              <country key="DZ"/>
            </address>
          </desc>
          <listRelation>
            <relation active="#struct-92851" type="direct"/>
          </listRelation>
        </org>
        <org type="laboratory" xml:id="struct-1051126" status="VALID">
          <orgName>Institut des Neurosciences Paris-Saclay</orgName>
          <orgName type="acronym">NeuroPSI</orgName>
          <date type="start">2020-01-01</date>
          <desc>
            <address>
              <addrLine>Centre National de la Recherche Scientifique, Unité Mixte de Recherche-9197 Université Paris-SaclayCampus CEA Saclay, Bât. 151151 route de la Rotonde91400 Saclay</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://neuropsi.cnrs.fr</ref>
          </desc>
          <listRelation>
            <relation active="#struct-419361" type="direct"/>
            <relation name="UMR9197" active="#struct-441569" type="direct"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-92851" status="VALID">
          <orgName>Université de Constantine</orgName>
          <desc>
            <address>
              <country key="DZ"/>
            </address>
          </desc>
        </org>
        <org type="institution" xml:id="struct-419361" status="VALID">
          <idno type="IdRef">241345251</idno>
          <idno type="ROR">https://ror.org/03xjwb503</idno>
          <orgName>Université Paris-Saclay</orgName>
          <desc>
            <address>
              <addrLine>Bâtiment Bréguet, 3 Rue Joliot Curie 2e ét, 91190 Gif-sur-Yvette</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.universite-paris-saclay.fr/fr</ref>
          </desc>
        </org>
        <org type="regroupinstitution" xml:id="struct-441569" status="VALID">
          <idno type="IdRef">02636817X</idno>
          <idno type="ISNI">0000000122597504</idno>
          <idno type="ROR">https://ror.org/02feahw73</idno>
          <orgName>Centre National de la Recherche Scientifique</orgName>
          <orgName type="acronym">CNRS</orgName>
          <date type="start">1939-10-19</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.cnrs.fr/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>