Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet

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dc.authoridUZEN, Huseyin/0000-0002-0998-2130
dc.authoridIMAK, Andac/0000-0002-3654-040X
dc.authorwosidUZEN, Huseyin/CZK-0841-2022
dc.contributor.authorCelebi, Adalet
dc.contributor.authorImak, Andac
dc.contributor.authorUzen, Huseyin
dc.contributor.authorBudak, Umit
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorHanbay, Davut
dc.contributor.authorSengur, Abdulkadir
dc.date.accessioned2024-08-04T20:54:38Z
dc.date.available2024-08-04T20:54:38Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjectives. This study, which uses artificial intelligence-based methods, aimed to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists. Methods. A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect the sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images. Results. The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, outperforming the state-of-the-art models. Conclusions. The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images. (Oral Surg Oral Med Oralen_US
dc.description.sponsorshipSmall and Medium Enterprises Development Organization of Turkey (KOSGEB) [62146]en_US
dc.description.sponsorshipThanks are owed to the Small and Medium Enterprises Development Organization of Turkey (KOSGEB) which supported the present study under R&D and Innovation Support Program project number 62146, Artificial intelligence-based expert system design in oral radiologic imaging techniques.en_US
dc.identifier.doi10.1016/j.oooo.2023.06.001
dc.identifier.endpage161en_US
dc.identifier.issn2212-4403
dc.identifier.issn2212-4411
dc.identifier.issue1en_US
dc.identifier.pmid37633787en_US
dc.identifier.scopus2-s2.0-85168995521en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage149en_US
dc.identifier.urihttps://doi.org/10.1016/j.oooo.2023.06.001
dc.identifier.urihttps://hdl.handle.net/11616/101547
dc.identifier.volume138en_US
dc.identifier.wosWOS:001259339800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Science Incen_US
dc.relation.ispartofOral Surgery Oral Medicine Oral Pathology Oral Radiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural-Networken_US
dc.subjectActive Contoursen_US
dc.subjectClassificationen_US
dc.subjectSegmentationen_US
dc.subjectRecognitionen_US
dc.subjectTeethen_US
dc.titleMaxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNeten_US
dc.typeArticleen_US

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