University of Aberdeen logo

AURA - Aberdeen University Research Archive

 

Lightweight deep learning methods for panoramic dental X-ray image segmentation

dc.contributor.authorLin, Songyue
dc.contributor.authorHao, Xuejiang
dc.contributor.authorLiu, Yan
dc.contributor.authorYan, Dong
dc.contributor.authorLiu, Jianwei
dc.contributor.authorZhong, Mingjun
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.date.accessioned2023-03-08T16:42:05Z
dc.date.available2023-03-08T16:42:05Z
dc.date.issued2022-12-16
dc.description.statusPeer revieweden
dc.format.extent1391659
dc.identifier227047849
dc.identifierca381812-926c-4282-ad05-5f13c454adc1
dc.identifier85144143331
dc.identifier.citationLin, S, Hao, X, Liu, Y, Yan, D, Liu, J & Zhong, M 2022, 'Lightweight deep learning methods for panoramic dental X-ray image segmentation', Neural Computing and Applications. https://doi.org/10.1007/s00521-022-08102-7en
dc.identifier.doi10.1007/s00521-022-08102-7
dc.identifier.issn0941-0643
dc.identifier.otherORCID: /0000-0002-1525-1270/work/130882036
dc.identifier.urihttps://hdl.handle.net/2164/20259
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85144143331&partnerID=8YFLogxKen
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applicationsen
dc.subjectArtificial intelligenceen
dc.subjectDeep learningen
dc.subjectImage segmentationen
dc.subjectLightweight neural networken
dc.subjectTooth segmentationen
dc.subjectX-rayen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectSoftwareen
dc.subjectArtificial Intelligenceen
dc.subjectSupplementary Dataen
dc.subject.lccQA75en
dc.titleLightweight deep learning methods for panoramic dental X-ray image segmentationen
dc.typeJournal articleen

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Lin_etal_NCA_Lightweight_Deep_Learning_VOR.pdf
Size:
1.33 MB
Format:
Adobe Portable Document Format

Collections