Teeth Detection Algorithm and Teeth Condition Classification Based on Convolutional Neural Networks for Dental Panoramic Radiographs

Lin, Nung-Hsiang; Lin, Ting-Lan; Wang, Xiaoyue; Kao, Wan-Ting; Tseng, Hua-Wei; Chen, Shih-Lun; Chiou, Yih‐Shyh; Lin, Szu-Yin; Villaverde, Jocelyn F.; Kuo, Yu-Fang

doi:10.1166/jmihi.2018.2354

Cited by 20 publications

(18 citation statements)

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“…They have utilized a pretrained VGG16 15 as feature detector. Lin et al 16 proposed an algorithm based on CNN to automatically detect teeth and classify their conditions in panoramic dental radiographs. In order to increase the amount of data, different data augmentation techniques such as flipping and random cropping are used.…”

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confidence: 99%

Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs

Mahdi

Motoki

Kobashi

2020

Sci Rep

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Computer-assisted analysis of dental radiograph in dentistry is getting increasing attention from the researchers in recent years. This is mainly because it can successfully reduce human-made error due to stress, fatigue or lack of experience. Furthermore, it reduces diagnosis time and thus, improves overall efficiency and accuracy of dental care system. An automatic teeth recognition model is proposed here using residual network-based faster R-CNN technique. The detection result obtained from faster R-CNN is further refined by using a candidate optimization technique that evaluates both positional relationship and confidence score of the candidates. It achieves 0.974 and 0.981 mAPs for ResNet-50 and ResNet-101, respectively with faster R-CNN technique. The optimization technique further improves the results i.e. F1 score improves from 0.978 to 0.982 for ResNet-101. These results verify the proposed method’s ability to recognize teeth with high degree of accuracy. To test the feasibility and robustness of the model, a tenfold cross validation (CV) is presented in this paper. The result of tenfold CV effectively verifies the robustness of the model as the average F1 score obtained is more than 0.970. Thus, the proposed model can be used as a useful and reliable tool to assist dental care professionals in dentistry.

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confidence: 99%

Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs

Mahdi

Motoki

Kobashi

2020

Sci Rep

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“…The results of the restorations model had an accuracy as high as 95.56%, while the accuracy in the judgment of caries was as high as 90.30%. These results represent a significant improvement over previously proposed methods in the literature [ 30 , 31 ]. These accuracies provided this study with the confidence to further extend model development for applied clinical medicine.…”

Section: Discussionmentioning

confidence: 51%

“…Table 6 and Table 7 correspond to the judgment of the upper and lower rows of teeth in Figure 10 . Results show that the accuracy of the proposed model for judging the restorations was 95.56%, which is an improvement compared to Lin et al [ 30 ], with an accuracy of 90.23%. The proposed method in Lin et al [ 30 ] first enhanced the classification features of the image, and then added the regular term and impulse, before establishing a CNN model with the ReLU function.…”

Section: Resultsmentioning

confidence: 69%

“…Results show that the accuracy of the proposed model for judging the restorations was 95.56%, which is an improvement compared to Lin et al [ 30 ], with an accuracy of 90.23%. The proposed method in Lin et al [ 30 ] first enhanced the classification features of the image, and then added the regular term and impulse, before establishing a CNN model with the ReLU function. With regard to judging caries, the proposed model in this study showed an accuracy of 90.30%, which was also an improvement compared to Singh and Sehgal [ 31 ], with an accuracy of 80.00%, which used a neural network classifier to classify caries.…”

Section: Resultsmentioning

confidence: 69%

“…Therefore, this paper chose it as the main network to classify the symptoms of disease. This study also compares the accuracy of different neural networks with the methods of reference papers Lin et al [ 30 ] and Chen et al [ 31 ], as shown in Table 12 .…”

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confidence: 99%

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Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Mao

Chen

Chou

et al. 2021

Sensors

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Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

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Design of a novel panoptic segmentation using multi-scale pooling model for tooth segmentation

Nagaraju,

Sudha

2024

Soft Comput

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Teeth Detection Algorithm and Teeth Condition Classification Based on Convolutional Neural Networks for Dental Panoramic Radiographs

Cited by 20 publications

References 0 publications

Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs

Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Design of a novel panoptic segmentation using multi-scale pooling model for tooth segmentation

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