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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45EA6NB
Repositorysid.inpe.br/sibgrapi/2021/09.14.23.12
Last Update2021:09.14.23.12.03 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.14.23.12.04
Metadata Last Update2022:09.10.00.16.17 (UTC) administrator
Citation KeyLouzadaPaul:2021:ClMoIm
TitleClassificando Modelos de Implantes Dentários Usando Redes Neurais Convolucionais com Dados Sintetizados
FormatOn-line
Year2021
Access Date2024, Apr. 19
Number of Files1
Size1411 KiB
2. Context
Author1 Louzada, Henrique Almeida
2 Paula, Maria Inês Lage de
Affiliation1 Pontifical Catholic University of Minas Gerais
2 Pontifical Catholic University of Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addresshenriquelouz@gmail.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2021-09-14 23:12:04 :: henriquelouz@gmail.com -> administrator ::
2022-09-10 00:16:17 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsdental implant
classification
computer vision
convolutional neural networks
AbstractClassifying dental implants in radiography images using Convolutional Neural Networks implies training them using images that are hardly publicly available. This work seeks to build a synthetic database of dental implants and test its effectiveness when using it to train one of these networks. Three different implant models were methodically photographed and basic Data Augmentation and Style Transfer techniques were used to create a training database. Some real X-ray images were collected to compose a test dataset and a simple Convolutional Neural Network was architected. Training this network with the synthetic set and testing it with the real set resulted in a predictive model with 71% overall accuracy, which highlights the possibility of using a synthetic database for this purpose. Implications for results and future work were discussed.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2021 > Classificando Modelos de...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45EA6NB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45EA6NB
Languagept
Target FileClassifying_Dental_Implant_Models_Using_Convolutional_Neural_Networks_on_Synthetized_Datasets.pdf
User Grouphenriquelouz@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 3
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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