VGG-16 딥러닝 알고리즘을 활용한 우식치아와 건전치아 분류
Evaluation of VGG-16 deep learning algorithm for dental caries classification
변민지, 전은주, 김지수, 황재준, 정승화,
소속 상세정보
변민지 ( Byon Min-Ji ) - Pusan National University School of Dentistry Department of Preventive and Community Dentistry
전은주 ( Jun Eun-Joo ) - Pusan National University School of Dentistry Department of Preventive and Community Dentistry
김지수 ( Kim Ji-Soo ) - Jeonju-Kijeon College Department of Dental Hygiene
황재준 ( Hwang Jae-Joon ) - Pusan National University School of Dentistry Department of Oral and Maxillofacial Radiology
정승화 ( Jeong Seung-Hwa ) - Pusan National University School of Dentistry Department of Preventive and Community Dentistry
Abstract
Objectives: Diagnosis of dental caries is based on the dentist’s observation and subjective judgment; therefore, a reliable and objective approach for diagnosing caries is required. Intraoral camera images combined with deep learning technology can be a useful tool to diagnose caries. This study aimed to evaluate the accuracy of the VGG-16 convolutional neural network (CNN) model in detecting dental caries in intraoral camera images.
Methods: Images were obtained from the Internet and websites using keywords linked to teeth and dental caries. The 670 images that were obtained were categorized by an investigator as either sound (404 sound teeth) or dental caries (266 dental caries), and used in this study. The training and test datasets were divided in the ratio of 7:3 and a four-fold cross validation was performed. The Tensorflow-based Python package Keras was used to train and validate the CNN model. Accuracy,Kappa value, sensitivity, specificity, positive predictive value, negative predictive value, ROC (receiver operating characteristic) curve and AUC (area under curve) values were calculated for the test datasets.
Results: The accuracy of the VGG-16 deep learning model for the four datasets, through random sampling, was between 0.77 and 0.81, with 0.81 being the highest. The Kappa value was 0.51-0.60, indicating moderate agreement. The resulting positive predictive values were 0.77-0.82 and negative predictive values were 0.80-0.85. Sensitivity, specificity, and AUC values were 0.66-0.74, 0.81-0.88, and 0.88-0.91, respectively.
Conclusions: The VGG-16 CNN model showed good discriminatory performance in detecting dental caries in intraoral camera images. The deep learning model can be beneficial in monitoring dental caries in the population.
키워드
CNN; Convolutional neural network; Deep learning; Dental caries classification; VGG-16
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