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dc.contributor.authorYaman, Orhan
dc.contributor.authorDogan, Sengul
dc.contributor.authorTuncer, Turker
dc.contributor.authorSubasi, Abdulhamit
dc.date.accessioned2023-03-13T06:14:31Z
dc.date.available2023-03-13T06:14:31Z
dc.date.issued2022-05-01
dc.identifier.citationYaman, Orhan and Dogan, Sengul and Tuncer, Turker and Subasi, Abdulhamit, Skin cancer classification model based on hybrid deep feature generation and iterative mRMR. In Computational Intelligence Based Solutions for Vision Systems, 2022en_US
dc.identifier.isbn978-0-7503-4821-8en_US
dc.identifier.doihttps://doi.org/10.1088/978-0-7503-4821-8en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/586
dc.description.abstractChapter 4 develops a hybrid deep feature extraction model based on five pre-trained deep learning models and an ImRMR based feature selection model for skin cancer classification.en_US
dc.publisherIOP Publishingen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectMachine Learningen_US
dc.subjectDeep Feature Generationen_US
dc.subjectIterative mRMRen_US
dc.titleSkin cancer classification model based on hybrid deep feature generation and iterative mRMRen_US
dc.source.booktitleComputational Intelligence Based Solutions for Vision Systemsen_US
dc.source.volume2053-2563;
dc.source.pages1-24en_US
dc.contributor.researcherExternal Collaborationen_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorYaman, Orhan


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