Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3522
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dc.contributor.authorBerkay M.-
dc.contributor.authorMergen E.H.-
dc.contributor.authorBinici R.C.-
dc.contributor.authorBayhan Y.-
dc.contributor.authorGungor A.-
dc.contributor.authorOkur E.-
dc.contributor.authorUnay D.-
dc.date.accessioned2023-06-16T15:00:42Z-
dc.date.available2023-06-16T15:00:42Z-
dc.date.issued2019-
dc.identifier.isbn9.78173E+12-
dc.identifier.urihttps://doi.org/10.1109/EBBT.2019.8741934-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3522-
dc.description2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 -- 24 April 2019 through 26 April 2019 -- 148870en_US
dc.description.abstractMelanoma which occurs with non-healing DNA degradation in melanocyte cells, is the most deadly type of skin cancers. Importantly, it can be identified for a treatment before it spreads to other tissues, i.e., early diagnosis. To identify, a specialist visually inspects whether the suspected lesion is melanoma or not. However, due to different education and experience levels of specialists or as a result of the patient not being in a facility that is specialized to this area, the problem of 'subjectivity' arises, and a good visual investigation accuracy may not always be achieved. Therefore, there is a significant need for automatic detection tools and systems. In this study, a method based on deep learning for automatic detection of melanoma from dermoscopic images is proposed. The developed system is tested with a large dataset and encouraging results are obtained. © 2019 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectDermoscopyen_US
dc.subjectMelanomaen_US
dc.subjectSkin canceren_US
dc.subjectBiomedical engineeringen_US
dc.subjectDeep learningen_US
dc.subjectDeep neural networksen_US
dc.subjectDiagnosisen_US
dc.subjectDiseasesen_US
dc.subjectElectronic medical equipmenten_US
dc.subjectLarge dataseten_US
dc.subjectNeural networksen_US
dc.subjectOncologyen_US
dc.subjectAutomatic Detectionen_US
dc.subjectConvolutional neural networken_US
dc.subjectDermoscopic imagesen_US
dc.subjectDermoscopyen_US
dc.subjectMelanomaen_US
dc.subjectMelanoma detectionen_US
dc.subjectSkin cancersen_US
dc.subjectVisual investigationen_US
dc.subjectDermatologyen_US
dc.titleDeep learning based melanoma detection from dermoscopic imagesen_US
dc.title.alternativeDermoskopik görüntülerden derin ö?renme tabanli melanom tespitien_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/EBBT.2019.8741934-
dc.identifier.scopus2-s2.0-85068548087en_US
dc.authorscopusid57209738253-
dc.authorscopusid57209735338-
dc.authorscopusid57209734622-
dc.authorscopusid57209731028-
dc.authorscopusid57195215602-
dc.authorscopusid55922238900-
dc.authorscopusid14069326000-
dc.identifier.wosWOS:000491430200039en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1tr-
item.cerifentitytypePublications-
crisitem.author.dept05.02. Biomedical Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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