Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging.
BMC Med Imaging
; 22(1): 66, 2022 04 08.
Article
em En
| MEDLINE
| ID: mdl-35395737
ABSTRACT
BACKGROUND:
This study aims to propose the combinations of image processing and machine learning model to segment the maturity development of the mandibular premolars using a Keras-based deep learning convolutional neural networks (DCNN) model.METHODS:
A dataset consisting of 240 images (20 images per stage per sex) of retrospect digital dental panoramic imaging of patients between 5 and 14 years of age was retrieved. In image preprocessing, abounding box with a dimension of 250 × 250 pixels was assigned to the left mandibular first (P1) and second (P2) permanent premolars. The implementation of dynamic programming of active contour (DP-AC) and convolutions neural network on images that require the procedure of image filtration using Python TensorFlow and Keras libraries were performed in image segmentation and classification, respectively.RESULTS:
Image segmentation using the DP-AC algorithm enhanced the visibility of the image features in the region of interest while suppressing the image's background noise. The proposed model has an accuracy of 97.74%, 96.63% and 78.13% on the training, validation, and testing set, respectively. In addition, moderate agreement (Kappa value = 0.58) between human observer and computer were identified. Nonetheless, a robust DCNN model was achieved as there is no sign of the model's over-or under-fitting upon the learning process.CONCLUSIONS:
The application of digital imaging and deep learning techniques used by the DP-AC and convolutions neural network algorithms to segment and identify premolars provides promising results for semi-automated forensic dental staging in the future.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Dente Pré-Molar
/
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
BMC Med Imaging
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Malásia