Deep Neural Networks for Dental Implant System Classification.
Biomolecules
; 10(7)2020 07 01.
Article
en En
| MEDLINE
| ID: mdl-32630195
ABSTRACT
In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Radiografía Panorámica
/
Interpretación de Imagen Radiográfica Asistida por Computador
/
Implantes Dentales
Límite:
Humans
Idioma:
En
Revista:
Biomolecules
Año:
2020
Tipo del documento:
Article
País de afiliación:
Japón