Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs

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dc.contributor.authorBui, Toan Huy
dc.contributor.authorHamamoto, Kazuhiko
dc.contributor.authorPaing, May Phu
dc.date.accessioned2026-08-06T10:37:56Z
dc.date.available2026-08-06T10:37:56Z
dc.date.issued2022-10-01
dc.description.abstractCaries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.
dc.identifier.citationEntropy, 24(10), 2022
dc.identifier.doi10.3390/e24101358
dc.identifier.issn10994300
dc.identifier.other2-s2.0-85140580970
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13467
dc.sourceEntropy
dc.subjectcaries screening
dc.subjectdeep learning
dc.subjectdental radiographs
dc.subjectensemble
dc.titleAutomated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs
dc.typeArticle

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