Numbering teeth in panoramic images: A novel method based on deep learning and heuristic algorithm

Basit Öğe Kaydı
dc.authoridKARAOGLU, Ahmet/0000-0002-7507-3031
dc.contributor.authorKaraoglu, Ahmet
dc.contributor.authorOzcan, Caner
dc.contributor.authorPekince, Adem
dc.contributor.authorYasa, Yasin
dc.date.accessioned2024-09-29T15:57:36Z
dc.date.available2024-09-29T15:57:36Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDental problems are one of the most common health problems for people. To detect and analyze these problems, dentists often use panoramic radiographs that show the entire mouth and have low radiation exposure and exposure time. Analyzing these radiographs is a lengthy and tedious process. Recent studies have ensured dental radiologists can perform the analyses faster with various artificial intelligence sup-ports. In this study, the numbering performance of Mask R-CNN and our heuristic algorithm-based method was verified on panoramic dental radiographs according to the Federation Dentaire Internationale (FDI) system. Ground-truth labelling of images required for training the deep learning algorithm was performed by two dental radiologists using the web-based labelling software DentiAssist created by the first author. The dataset was created from 2702 anonymized panoramic radio-graphs. The dataset is divided into 1747, 484, and 471 images, which serve as training, validation, and test sets. The dataset was validated using the k-fold cross-validation method (k = 5). A three-step heuristic algorithm was developed to improve the Mask R-CNN segmentation and numbering results. As far as we know, our study is the first in the literature to use a heuristic method in addition to traditional deep learning algorithms in detection, segmentation and numbering studies in panoramic radiography. The experimental results show that the mAp (@IOU = 0.5), precision, recall and f1 scores are 92.49%, 96.08%, 95.65% and 95.87%, respectively. The results of the learning-based algorithm were improved by more than 4%. In our research, we discovered that heuristic algorithms could improve the accuracy of deep learning-based algorithms. Our research will significantly reduce dental radiologists' workload, speed up diagnostic processes, and improve the accuracy of deep learning systems.(c) 2022 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) as part of the DentiAssist project [2200272]en_US
dc.description.sponsorshipThis study is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) as part of the DentiAssist project numbered 2200272.en_US
dc.identifier.doi10.1016/j.jestch.2022.101316
dc.identifier.issn2215-0986
dc.identifier.scopus2-s2.0-85144841640en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2022.101316
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4915
dc.identifier.volume37en_US
dc.identifier.wosWOS:000974472500001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltden_US
dc.relation.ispartofEngineering Science and Technology-An International Journal-Jestechen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectHeuristic algorithmen_US
dc.subjectMask R-CNNen_US
dc.subjectPanoramic radiographsen_US
dc.subjectSegmentationen_US
dc.subjectNumberingen_US
dc.titleNumbering teeth in panoramic images: A novel method based on deep learning and heuristic algorithmen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu