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  <doi_batch_id>aspg-3-1014-1791417438</doi_batch_id>
  <timestamp>20261007235718</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <journal>
   <journal_metadata language="en">
    <full_title>Fusion: Practice and Applications</full_title>
    <abbrev_title>FPA</abbrev_title>
    <issn media_type="print">2770-0070</issn>
    <issn media_type="electronic">2692-4048</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2022</year>
    </publication_date>
    <journal_volume>
     <volume>7</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Support System Based Computer-Aided Detection for Skin Cancer: A Review</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Nechirvan Asaad</given_name>
      <surname>Zebari</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Engineering, Harran University, Sanliurafa, 63300, Turkey</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mehmet Emin</given_name>
      <surname>Tenekeci</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Engineering, Harran University, Sanliurafa, 63300, Turkey</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>According to the American Society of Clinical Oncology, Computer-Aided Diagnosis (CAD) techniques have the tremendous possibility for the screening and early identification of melanoma. They are evaluated in terms of their current state-of-the-art, as well as current practices, challenges, and prospects in the areas of image screening, pre-processing of an image, segmentation of Region of Interest (ROI), feature extraction, feature selection, and classification of dermoscopic images. It is stated in this study that statistical information and outcomes from the most major implementations that have been reported to date are presented. We investigated the evaluation performance of many classifiers that had been developed specifically for the diagnosis of skin cancer. The fundamental aim of this paper is to develop a framework that will serve as a complete guideline for choosing relevant techniques for various elements of an automatic detection technique.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2022</year>
    </publication_date>
    <pages>
     <first_page>30</first_page>
     <last_page>40</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1014</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/FPA.070103</doi>
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