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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/45EA6NB
Repositorysid.inpe.br/sibgrapi/2021/09.14.23.12
Last Update2021:09.14.23.12.03 (UTC) henriquelouz@gmail.com
Metadatasid.inpe.br/sibgrapi/2021/09.14.23.12.04
Metadata Last Update2021:09.14.23.12.04 (UTC) henriquelouz@gmail.com
Citation KeyLouzadaPaul:2021:ClMoIm
TitleClassificando Modelos de Implantes Dentários Usando Redes Neurais Convolucionais com Dados Sintetizados
FormatOn-line
Year2021
Access Date2021, Sep. 24
Number of Files1
Size1411 KiB
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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 (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeUndergraduate Work
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
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data URLhttp://urlib.net/rep/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
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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