Publication:
Deep Learning for Midfacial Fracture Detection in CT Images

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dc.contributor.authorWarin K.
dc.contributor.authorVicharueang S.
dc.contributor.authorJantana P.
dc.contributor.authorLimprasert W.
dc.contributor.authorThanathornwong B.
dc.contributor.authorSuebnukarn S.
dc.contributor.correspondenceWarin K.
dc.contributor.otherSrinakharinwirot University
dc.date.accessioned2025-05-28T07:55:10Z
dc.date.issued2024-01-25
dc.date.issuedBE2567-01-25
dc.description.abstractThis study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object detection models were created using faster R-CNN and RetinaNet from 2,000 CT images. The best detection model, faster R-CNN, yielded an average precision of 0.79 and an area under the curve (AUC) of 0.80. In conclusion, faster R-CNN model has good potential for detecting midfacial fractures in CT images.
dc.identifier.citationStudies in Health Technology and Informatics Vol.310 (2024) , 1497-1498
dc.identifier.doi10.3233/SHTI231262
dc.identifier.eissn18798365
dc.identifier.issn09269630
dc.identifier.pmid38269714
dc.identifier.scopus2-s2.0-85183576645
dc.identifier.urihttps://hdl.handle.net/20.500.14740/20219
dc.rights.holderSCOPUS
dc.subjectEngineering
dc.subjectMedicine
dc.subjectHealth Professions
dc.titleDeep Learning for Midfacial Fracture Detection in CT Images
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.endPage1498
oaire.citation.startPage1497
oaire.citation.titleStudies in Health Technology and Informatics
oaire.citation.volume310
oairecerif.author.affiliationCollege of Interdisciplinary Studies, Thammasat University
oairecerif.author.affiliationThammasat University
oairecerif.author.affiliationSrinakharinwirot University
oairecerif.author.affiliationStoremesh Research
swu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85183576645&origin=inward

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Scopus: Year 2024