Journal article Open Access

Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks

Tasmiya Tazeen; Mrinal Sarvagya


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  <identifier identifierType="URL">https://zenodo.org/record/5408324</identifier>
  <creators>
    <creator>
      <creatorName>Tasmiya Tazeen</creatorName>
      <affiliation>School of Electronics and Communication Engineering, Reva University, Bengaluru-560064, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Mrinal Sarvagya</creatorName>
      <affiliation>School of Electronics and Communication Engineering, Reva University, Bengaluru-560064, India.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Segmentation, Brain Tumor, Convolutional  Neural Network, Deep Learning.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">100.1/ijeat.F29480810621</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2021-08-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5408324</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.F2948.0810621</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.&lt;/p&gt;</description>
  </descriptions>
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