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Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network.
Bispo, Mayara Simões; Pierre Júnior, Mário Lúcio Gomes de Queiroz; Apolinário, Antônio Lopes; Dos Santos, Jean Nunes; Junior, Braulio Carneiro; Neves, Frederico Sampaio; Crusoé-Rebello, Iêda.
Afiliação
  • Bispo MS; Postgraduate Program in Dentistry and Health, Federal University of Bahia, Salvador, Brazil.
  • Pierre Júnior MLGQ; Computer Science Department, Federal Institute of Education, Science and Technology of Bahia, Senhor do Bonfim, Bahia, Brazil.
  • Apolinário AL; Computer Science Department, Federal University of Bahia, Salvador, Brazil.
  • Dos Santos JN; Division of Oral Pathology, Federal University of Bahia, Salvador, Brazil.
  • Junior BC; Division of Oral and Maxillofacial Surgery, Southwest Bahia State University, Vitória da Conquista, Brazil.
  • Neves FS; Division of Oral and Maxillofacial Radiology, Federal University of Bahia, Salvador, Brazil.
  • Crusoé-Rebello I; Division of Oral and Maxillofacial Radiology, Federal University of Bahia, Salvador, Brazil.
Dentomaxillofac Radiol ; 50(7): 20210002, 2021 Oct 01.
Article em En | MEDLINE | ID: mdl-33882255
OBJECTIVE: To analyse the automatic classification performance of a convolutional neural network (CNN), Google Inception v3, using tomographic images of odontogenic keratocysts (OKCs) and ameloblastomas (AMs). METHODS: For construction of the database, we selected axial multidetector CT images from patients with confirmed AM (n = 22) and OKC (n = 18) based on a conclusive histopathological report. The images (n = 350) were segmented manually and data augmentation algorithms were applied, totalling 2500 images. The k-fold × five cross-validation method (k = 2) was used to estimate the accuracy of the CNN model. RESULTS: The accuracy and standard deviation (%) of cross-validation for the five iterations performed were 90.16 ± 0.95, 91.37 ± 0.57, 91.62 ± 0.19, 92.48 ± 0.16 and 91.21 ± 0.87, respectively. A higher error rate was observed for the classification of AM images. CONCLUSION: This study demonstrated a high classification accuracy of Google Inception v3 for tomographic images of OKCs and AMs. However, AMs images presented the higher error rate.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ameloblastoma / Neoplasias Maxilomandibulares / Cistos Odontogênicos Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Dentomaxillofac Radiol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ameloblastoma / Neoplasias Maxilomandibulares / Cistos Odontogênicos Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Dentomaxillofac Radiol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido