# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 1 Introduction
This improvement was particularly noticeable in their ability to detect initial and moderate caries. Recently, [PERSON] et al. [39] provided an architecture for the Dental Caries Detection Network (DCDNet) to segment dental caries. The main characteristic of DCDNet is the presence of a Multi-Predicted Output (MPO) structure at the end. This is what makes it different from other segmentation architectures. [PERSON] et al. [40] combined the adult dataset with the children's dataset in order to show that, despite significantly increasing the number of children's datasets, the adult dataset did not enhance the experimental effects of the children's dental dataset. The literature review reveals that previous studies have explored various methods for detecting and classifying dental caries. In addition to the research mentioned above, there are still some research gaps that need to be addressed in the field of dental cavity detection. Although some previous studies have focused on classifying teeth as decayed or normal, there is a need for a more specific and detailed identification of the different stages of dental caries, such as initial, moderate, or extensive caries. This level of classification can provide valuable information to make more targeted and effective decisions. One popular deep learning approach for image segmentation, used in [8; 14; 36; 38] is the U-Net architecture. Several variants of U-Net have been proposed to improve its performance in dental caries segmentation, basically using either [21] or [22] as a base model. U-Net utilizes an encoder-decoder structure with skip connections, which allows for the combination of high-resolution features with the up-sampled output, resulting in improved segmentation performance. Furthermore, in addition to assessing the detection performance of these models, several studies have also compared their diagnostic performance with that of clinicians, such as [8]. The results have shown that deep learning models can significantly improve clinicians' diagnostic performance in detecting dental caries and lesions on dental X-ray images. These findings highlight the potential of deep learning models to serve as valuable tools for clinicians with the diagnosis and treatment planning of dental caries [28]. Overall, a significant body of research has been conducted on the application of deep learning models in dental caries detection and diagnosis and has shown promising results [8]. However, one of the primary difficulties in segmenting dental caries, as mentioned in [8; 11; 36], is the unequal distribution of exposure to X-rays and variations in tooth density, thickness, or reflections of various structures and organs, particularly in panoramic images. These factors introduce irregularities into radiographic images, posing challenges for conventional segmentation techniques to precisely detect and outline carious lesions. In conclusion, the use of DL methods in dental cavity detection has shown promise in improving accuracy and reducing the workload of dentists. Various studies have explored different DL architectures such as NN and CNN to detect and classify dental caries in X-rayimages. These methods have demonstrated good detection performance, with accuracy scores ranging from 97% to 93.61%.