Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/8878
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Caylak, Tulin | - |
dc.contributor.author | Yetik, Imam Samil | - |
dc.contributor.author | Culhaoglu, Ahmet | - |
dc.contributor.author | Orhan, Kaan | - |
dc.contributor.author | Kilicarslan, Mehmet Ali | - |
dc.date.accessioned | 2022-11-30T19:23:02Z | - |
dc.date.available | 2022-11-30T19:23:02Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 978-1-6654-9578-3 | - |
dc.identifier.issn | 2157-8672 | - |
dc.identifier.uri | https://doi.org/10.1109/IWSSIP55020.2022.9854463 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/8878 | - |
dc.description | 29th International Conference on Systems, Signals and Image Processing (IWSSIP) -- JUN 01-03, 2022 -- Sofia, BULGARIA | en_US |
dc.description.abstract | Over 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.sponsorship | Tech Univ Sofia,IEEE Bulgaria Sect,IEEE CAS SSC Joint Bulgarian Chapter,Tech Univ Sofia, Res Dev Sector | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2022 29th International Conference On Systems, Signals and Image Processing (Iwssip) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | medical image segmentation | en_US |
dc.subject | U-Net | en_US |
dc.subject | Inception-ResNet-V2 | en_US |
dc.subject | transfer learning | en_US |
dc.subject | panoramic x-rays | en_US |
dc.subject | dice coefficient | en_US |
dc.subject | deep learning | en_US |
dc.title | Automated Dental Panoramic Image Segmentation Using Transfer Learning Based Cnns | en_US |
dc.type | Conference Object | en_US |
dc.identifier.wos | WOS:000859804900046 | en_US |
dc.identifier.scopus | 2-s2.0-85137160896 | en_US |
dc.identifier.doi | 10.1109/IWSSIP55020.2022.9854463 | - |
dc.authorscopusid | 57871822900 | - |
dc.authorscopusid | 57195245742 | - |
dc.authorscopusid | 57191754896 | - |
dc.authorscopusid | 8502419700 | - |
dc.authorscopusid | 8885711600 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.ozel | 2022v3_Edit | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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