Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8878
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dc.contributor.authorCaylak, Tulin-
dc.contributor.authorYetik, Imam Samil-
dc.contributor.authorCulhaoglu, Ahmet-
dc.contributor.authorOrhan, Kaan-
dc.contributor.authorKilicarslan, Mehmet Ali-
dc.date.accessioned2022-11-30T19:23:02Z-
dc.date.available2022-11-30T19:23:02Z-
dc.date.issued2022-
dc.identifier.isbn978-1-6654-9578-3-
dc.identifier.issn2157-8672-
dc.identifier.urihttps://doi.org/10.1109/IWSSIP55020.2022.9854463-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8878-
dc.description29th International Conference on Systems, Signals and Image Processing (IWSSIP) -- JUN 01-03, 2022 -- Sofia, BULGARIAen_US
dc.description.abstractOver the years, deep learning technology improved considerably, and its application areas have expanded. At the same time, the content and size of the datasets used for deep learning have also increased. However, this is not the case for dental datasets. Therefore, in this paper the transfer learning method was used to overcome this disadvantage. Segmentation was performed on 131 dental panoramic X-ray images with two different models based on the transfer learning method. The first model was constructed with pre-trained U-Net using the chest X-ray dataset. As the second model, the pre-trained Inception-ResNet-v2 structure was used. The performances of the methods we developed were compared visually and quantitatively using dice coefficient, accuracy, and intersection over union. While the dice coefficient success of the first model was 87.12%, the success of the second model reached 90.26%. Our new approach of using transfer learning for dental image segmentation proved to be very successful.en_US
dc.description.sponsorshipTech Univ Sofia,IEEE Bulgaria Sect,IEEE CAS SSC Joint Bulgarian Chapter,Tech Univ Sofia, Res Dev Sectoren_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 29th International Conference On Systems, Signals and Image Processing (Iwssip)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectmedical image segmentationen_US
dc.subjectU-Neten_US
dc.subjectInception-ResNet-V2en_US
dc.subjecttransfer learningen_US
dc.subjectpanoramic x-raysen_US
dc.subjectdice coefficienten_US
dc.subjectdeep learningen_US
dc.titleAutomated Dental Panoramic Image Segmentation Using Transfer Learning Based Cnnsen_US
dc.typeConference Objecten_US
dc.identifier.wosWOS:000859804900046en_US
dc.identifier.scopus2-s2.0-85137160896en_US
dc.identifier.doi10.1109/IWSSIP55020.2022.9854463-
dc.authorscopusid57871822900-
dc.authorscopusid57195245742-
dc.authorscopusid57191754896-
dc.authorscopusid8502419700-
dc.authorscopusid8885711600-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.ozel2022v3_Editen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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