# Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones
## 4 Discussion
For example, we aim to fine-tune the backbone network of the Faster R-CNN model to deal with indistinct contour problems and adjust the training method for the \"regional proposal network\" module to improve the ability to locate small objects [32]. High-resolution, standardized, and single-tooth photographs require professional cameras, expensive macro lenses, and experienced photographers. From the community's application point of view, it is impractical and cannot be obtained by normal people who want to use an application to check their teeth. A recent study by [PERSON], which used intraoral photos applying deep learning to diagnose tooth decay, achieved more than 90% agreement in the detection of caries. However, photographs are single-tooth photos taken with professional cameras and lenses (Nikon D300, D7100, and D7200, Nikon Micro 105-mm) [18]. Recently, there is one study in which YOLOv3 was used to detect dental caries via smartphone intraoral photos [12]. The study showed the mean average precision value (of 3 times of testing) of the YOLOv3 algorithm was 56.20%, which was lower than our study regarding cavitated caries detection (74%). The indirect comparison should be interpreted with caution since the two studies used two different datasets. In this study, algorithms YOLOv3 and Faster R-CNN performed better than algorithms RetinaNet and SSD with the data of caries and initial caries lesions diagnosis problems. The results could imply that the theoretical improvements proposed for algorithms RetinaNet and SSD may not be suitable for the distinctive feature of the current data, which contained small and very small objects. Of course, to conclude on the cause of the outperformance of algorithms YOLOv3 and Faster R-CNN will need other in-depth studies on each of the different processing characteristics of the algorithms. However, the research results point to the great potential of YOLOv3 and Faster R-CNN in practical applications. The results of the current study need to be improved for clinical application, especially in detecting initial caries. Most of the research direction in this field is shifting from the laboratory to clinical application. First, under ideal conditions and facilities, the deep learning model achieves very high accuracy. The next step is trying to maintain acceptable accuracy when testing under unfavored real-world conditions. In our approach, we sought to conduct an initial study with conditions that closely resemble those in the real community and then attempt to improve the accuracy in further stages. Several shortcomings need to be improved in further studies. First, input photos need to be enhanced to obtain a better quality of photos. Even though iPhone 7 was selected due to its popularity and affordable price, the gear camera failed to provide photos with good enough quality. A recent study showed an enhancement of initial photos could significantly improve the performance of the model [12]. This approach should be considered in future studies using smartphone photos as input. Secondly, the number of images for machine learning training needs to increase. Thirdly, the potential algorithms will be modified with the aim of dealing with the small-object detection problem. There might be a long way to go until there is a fast, accurate deep learning algorithm that uses intraoral, unstandardized photos taken from a  
Figure 5: Faster R-CNN misdiagnosed a light reflection as a non-cavitated dental caries (green box). universal device to diagnose dental caries. Our study provides researchers and engineers with a view of the performance of four deep learning models in detecting dental caries.