1 Ocak 2022 Konferans bildirisi Açık Erişim

Torun, Hulya; Batur, Numan; Bilgin, Buse; Esengur, Omer Tarik; Baysal, Kemal; Kulac, Ibrahim; Solaroglu, Ihsan; Onbasli, Mehmet Cengiz

Dublin Core

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  <dc:creator>Torun, Hulya</dc:creator>
  <dc:creator>Batur, Numan</dc:creator>
  <dc:creator>Bilgin, Buse</dc:creator>
  <dc:creator>Esengur, Omer Tarik</dc:creator>
  <dc:creator>Baysal, Kemal</dc:creator>
  <dc:creator>Kulac, Ibrahim</dc:creator>
  <dc:creator>Solaroglu, Ihsan</dc:creator>
  <dc:creator>Onbasli, Mehmet Cengiz</dc:creator>
  <dc:date>2022-01-01</dc:date>
  <dc:description>Glioblastoma is the most malignant and common high-grade brain tumor with a 14-month overall survival length. According to recent World Health Organization Central Nervous System tumor classification (2021), the diagnosis of glioblastoma requires extensive molecular genetic tests in addition to the traditional histopathological analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Time-consuming and expensive molecular tests as well as the need for clinical neuropathology expertise are the challenges in the diagnosis of glioblastoma. Hence, an automated and rapid analytical detection technique for identifying brain tumors from healthy tissues is needed to aid pathologists in achieving an error-free diagnosis of glioblastoma in clinics.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/259801</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:259801</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Machine Learning-Based Approach to Identify Formalin-Fixed Paraffin-Embedded Glioblastoma and Healthy Brain Tissues</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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