1 Aralık 2024 Dergi makalesi Açık Erişim

Korkut, Şerife Gül; Kocabaş, Hatice; Kurban, Rifat

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/274178</identifier>
  <creators>
    <creator>
      <creatorName>Korkut, Şerife Gül</creatorName>
      <givenName>Şerife Gül</givenName>
      <familyName>Korkut</familyName>
    </creator>
    <creator>
      <creatorName>Kocabaş, Hatice</creatorName>
      <givenName>Hatice</givenName>
      <familyName>Kocabaş</familyName>
    </creator>
    <creator>
      <creatorName>Kurban, Rifat</creatorName>
      <givenName>Rifat</givenName>
      <familyName>Kurban</familyName>
    </creator>
  </creators>
  <titles>
    <title>A Comparative Analysis Of Convolutional Neural Network Architectures For Binary Image Classification: A Case Study In Skin Cancer Detection</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2024</publicationYear>
  <subjects>
    <subject>Convolutional Neural Networks</subject>
    <subject>Binary Image Classification</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2024-12-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/274178</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.31466/kfbd.1515451</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this study, a comprehensive comparative analysis of Convolutional Neural Network (CNN) architectures for binary image classification is presented with a particular focus on the benefits of transfer learning. The performance and accuracy of prominent CNN models, including MobileNetV3, VGG19, ResNet50, and EfficientNetB0, in classifying skin cancer from binary images are evaluated. Using a pre-trained approach, the impact of transfer learning on the effectiveness of these architectures and identify their strengths and weaknesses within the context of binary image classification are investigated. This paper aims to provide valuable insights for selecting the optimal CNN architecture and leveraging transfer learning to achieve superior performance in binary image classification applications, particularly those related to medical image analysis.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
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