Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/3522
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Berkay M. | - |
dc.contributor.author | Mergen E.H. | - |
dc.contributor.author | Binici R.C. | - |
dc.contributor.author | Bayhan Y. | - |
dc.contributor.author | Gungor A. | - |
dc.contributor.author | Okur E. | - |
dc.contributor.author | Unay D. | - |
dc.date.accessioned | 2023-06-16T15:00:42Z | - |
dc.date.available | 2023-06-16T15:00:42Z | - |
dc.date.issued | 2019 | - |
dc.identifier.isbn | 9.78173E+12 | - |
dc.identifier.uri | https://doi.org/10.1109/EBBT.2019.8741934 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14365/3522 | - |
dc.description | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 -- 24 April 2019 through 26 April 2019 -- 148870 | en_US |
dc.description.abstract | Melanoma 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.iso | tr | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Dermoscopy | en_US |
dc.subject | Melanoma | en_US |
dc.subject | Skin cancer | en_US |
dc.subject | Biomedical engineering | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | Diagnosis | en_US |
dc.subject | Diseases | en_US |
dc.subject | Electronic medical equipment | en_US |
dc.subject | Large dataset | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Oncology | en_US |
dc.subject | Automatic Detection | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Dermoscopic images | en_US |
dc.subject | Dermoscopy | en_US |
dc.subject | Melanoma | en_US |
dc.subject | Melanoma detection | en_US |
dc.subject | Skin cancers | en_US |
dc.subject | Visual investigation | en_US |
dc.subject | Dermatology | en_US |
dc.title | Deep learning based melanoma detection from dermoscopic images | en_US |
dc.title.alternative | Dermoskopik görüntülerden derin ö?renme tabanli melanom tespiti | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/EBBT.2019.8741934 | - |
dc.identifier.scopus | 2-s2.0-85068548087 | en_US |
dc.authorscopusid | 57209738253 | - |
dc.authorscopusid | 57209735338 | - |
dc.authorscopusid | 57209734622 | - |
dc.authorscopusid | 57209731028 | - |
dc.authorscopusid | 57195215602 | - |
dc.authorscopusid | 55922238900 | - |
dc.authorscopusid | 14069326000 | - |
dc.identifier.wos | WOS:000491430200039 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | N/A | - |
dc.identifier.wosquality | N/A | - |
item.grantfulltext | reserved | - |
item.openairetype | Conference Object | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | tr | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 05.02. 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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File | Size | Format | |
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2615.pdf Restricted Access | 496.41 kB | Adobe PDF | View/Open Request a copy |
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