A Deep Learning Approach for Classification of Dentinal Tubule Occlusions

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dc.authoridKarayurek, Fatih/0000-0003-0602-7610
dc.contributor.authorDuru, Anday
dc.contributor.authorKaras, Ismail Rakip
dc.contributor.authorKarayurek, Fatih
dc.contributor.authorGulses, Aydin
dc.date.accessioned2024-09-29T16:02:43Z
dc.date.available2024-09-29T16:02:43Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study aimed to develop a novel deep learning model for reliable quantification of dentinal tubule occlusions instead of manual assessment techniques, and the performance of the model was compared to other methods in the literature. Ninety-six dentin samples were cut and prepared with desensitizing agents to occlude dentinal tubules on different levels. After obtaining images via scanning electron microscope (SEM), 2793 single dentinal tubule images with 48 x 48 resolution were segmented and labeled. Data augmentation techniques were applied for improvement in the learning rate. The augmented data having a total of 10700 images belonging to five classes were used as the network training dataset. The proposed convolutional neural network (CNN) is a class of deep learning model and was able to classify the degree of dentinal tubule occlusions into five classes with an overall accuracy rate of 90.24%. This paper primarily focuses on developing a CNN architecture for detecting the level of dentin tubule occlusions imaged by SEM. The results showed that the proposed CNN architecture is an immensely successful alternative and allowed for objective and automatic classification of segmented dentinal tubule images.en_US
dc.identifier.doi10.1080/08839514.2022.2094446
dc.identifier.issn0883-9514
dc.identifier.issn1087-6545
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85133863117en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1080/08839514.2022.2094446
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5681
dc.identifier.volume36en_US
dc.identifier.wosWOS:000819981800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofApplied Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCalcium Sodium Phosphosilicateen_US
dc.subjectFunctional Architectureen_US
dc.subjectReceptive-Fieldsen_US
dc.subjectIn-Situen_US
dc.subjectHypersensitivityen_US
dc.subjectNeocognitronen_US
dc.subjectPrevalenceen_US
dc.subjectModelen_US
dc.titleA Deep Learning Approach for Classification of Dentinal Tubule Occlusionsen_US
dc.typeArticleen_US

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