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Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks
- Source :
- Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 15, Sensors, Vol 21, Iss 5192, p 5192 (2021)
- Publication Year :
- 2021
-
Abstract
- Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation—more specifically, bitewing images—are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.
- Subjects :
- Dental radiography
diagnosis
Computer science
Dental Caries Susceptibility
Radiography
Population
Image processing
TP1-1185
02 engineering and technology
Dental Caries
Biochemistry
Convolutional neural network
Article
Analytical Chemistry
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Electrical and Electronic Engineering
education
Instrumentation
Radiography, Bitewing
caries
education.field_of_study
dentistry
medicine.diagnostic_test
Artificial neural network
business.industry
Chemical technology
dental radiography
Pattern recognition
030206 dentistry
neural networks
artificial intelligence
Atomic and Molecular Physics, and Optics
Test set
bitewing radiography
020201 artificial intelligence & image processing
Artificial intelligence
Neural Networks, Computer
business
Tooth
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 15
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....2f66fe3fdafe539c0cce4cb60042be19