Identification of enamel demineralization using high performance convolutional neural network

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dc.contributor.authorCARAMEL-JUVINO, AMANDApt_BR
dc.contributor.authorFAROOQ, SAJIDpt_BR
dc.contributor.authorROMANO, MARIANApt_BR
dc.contributor.authorZEZELL, DENISE M.pt_BR
dc.coverageInternacionalpt_BR
dc.creator.eventoSBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCEpt_BR
dc.date.accessioned2023-01-26T15:39:35Z
dc.date.available2023-01-26T15:39:35Z
dc.date.eventoOctober 13-15, 2022pt_BR
dc.description.abstractHere, we traces use segmentation and convolutional neural network (CNN) to trace, diagnose and quantify enamel demineralization for research. The preprocessing, histograms based methods are used to enhance the contrast and equalize the brightness through the scanning electron microscope images. Our result evidence that the deep learning based CNN model is highly efficient to process the dental image to achieve high accuracy of enamel demineralization and presents promising outcomes with optimal precision.pt_BR
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsorshipIDFAPESP: 17/50332-0pt_BR
dc.description.sponsorshipIDCNPq: INCT 465763/2014-6; Sisfoton MCTI 440228/2021-2pt_BR
dc.description.sponsorshipIDCAPES: PROCAD 001; 88881.068505/2014-01pt_BR
dc.event.siglaSBFOTON IOPCpt_BR
dc.identifier.citationCARAMEL-JUVINO, AMANDA; FAROOQ, SAJID; ROMANO, MARIANA; ZEZELL, DENISE M. Identification of enamel demineralization using high performance convolutional neural network. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, October 13-15, 2022, Recife, PE. <b>Proceedings...</b> Piscataway, NJ, USA: IEEE, 2022. DOI: <a href="https://dx.doi.org/10.1109/SBFotonIOPC54450.2022.9992381">10.1109/SBFotonIOPC54450.2022.9992381</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/33668.
dc.identifier.doi10.1109/SBFotonIOPC54450.2022.9992381pt_BR
dc.identifier.orcid0000-0001-7404-9606pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-7404-9606
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/33668
dc.localPiscataway, NJ, USApt_BR
dc.local.eventoRecife, PEpt_BR
dc.publisherIEEEpt_BR
dc.rightsopenAccesspt_BR
dc.subjectneural networks
dc.subjectdentistry
dc.subjectenamels
dc.subjectdemineralization
dc.subjectlearning
dc.titleIdentification of enamel demineralization using high performance convolutional neural networkpt_BR
dc.typeTexto completo de eventopt_BR
dspace.entity.typePublication
ipen.autorSAJID FAROOQ
ipen.autorDENISE MARIA ZEZELL
ipen.autorAMANDA CARAMEL JUVINO
ipen.codigoautor15722
ipen.codigoautor693
ipen.codigoautor15033
ipen.contributor.ipenauthorSAJID FAROOQ
ipen.contributor.ipenauthorDENISE MARIA ZEZELL
ipen.contributor.ipenauthorAMANDA CARAMEL JUVINO
ipen.date.recebimento23-01
ipen.event.datapadronizada2022pt_BR
ipen.identifier.ipendoc29302pt_BR
ipen.notas.internasProceedingspt_BR
ipen.type.genreArtigo
relation.isAuthorOfPublication60d3fba4-40e1-482c-9eda-4530bc63fecb
relation.isAuthorOfPublicationa565f8ad-3432-4891-98c0-a587f497db21
relation.isAuthorOfPublication2b3b78e2-0d00-4b2d-b29c-648c0e4b7f54
relation.isAuthorOfPublication.latestForDiscovery2b3b78e2-0d00-4b2d-b29c-648c0e4b7f54
sigepi.autor.atividadeZEZELL, DENISE M.:693:920:Npt_BR
sigepi.autor.atividadeFAROOQ, SAJID:15722:920:Npt_BR
sigepi.autor.atividadeCARAMEL-JUVINO, AMANDA:15033:920:Spt_BR
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