# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 1 Introduction
The dataset utilized in this investigation consisted of 3000 periapical images, with 2400 images in the training set and 600 in the testing set. The accuracy for detecting premolar caries was 89.0 percent, while for molar caries, it was 88.0 percent. The combined premolar and molar model achieved an accuracy of 82.0 percent. Notably, the premolar model exhibited the highest Area Under the Curve (AUC) at 0.917, demonstrating its strong performance in caries detection in periapical X-ray images. This study highlights the effectiveness of deep CNN algorithms in this application. Similarly, in another study conducted by [PERSON] et al. [32], a deep learning system based on a CNN was developed and evaluated for the detection of dental caries. The system was trained and tested on a dataset comprising 3932 photographs taken by 625 volunteers. Other researchers aim to solve the problem of cavity detection by providing a prediction of the needed treatment. [PERSON] et al. [33] proposed an automatic method to detect dental decay in order to predict the necessary treatment. This method predicted three types of treatments: (1) fluoride, (2) filling, and (3) root canal treatments. The proposed method classified the X-ray images among the three treatments. The overall accuracy of the model reached 87 percent. The best prediction was for the fluoride treatment with an accuracy score of 98 percent, followed by the root canal detection with an accuracy score of 88 percent. The filling treatment gave a lower classification accuracy score of 77 percent. Some researchers viewed the cavity detection problem as being one of segmentation. U-Net is widely applied for segmentation tasks in medical imaging [21; 34]. In the field of dental image analysis, several notable studies have utilized the U-Net architecture. [PERSON] et al. [35] employed the U-Net with CNN to accurately segment dental panoramic X-ray images, achieving superior results compared to other segmentation methods. They used the Ivisionlab dataset, which includes both X-ray and ground truth images. Furthermore, [PERSON] et al. [14] proposed an innovative algorithm that integrates oral anatomical knowledge with deep learning to segment CBCT and detect lesion. They improved on traditional U-Net by incorporating Fully Convolutional DenseNet (Dense U-Net), demonstrating enhanced lesion detection accuracy. Furthermore, [PERSON] et al. [36] focused on detecting caries lesions on bitewing X-rays using the U-Net model, achieving a pixel-wise marking accuracy of 0.80 percent with the Intersection-over-Union metric. Later, [PERSON] et al. [8] applied the U-Net model for the detection of dental caries using bitewing radiographs. Their results showed a precision score of 63.29 percent, a recall score of 65.02 percent, and an F1 score of 64.14 percent. In particular, dentist diagnostic performance improved when using the CNN model's results as a reference for caries detection, especially in proximal regions that are challenging to identify due to various factors, such as uneven exposure to X-rays and natural variability in tooth density or thickness. In addition, [PERSON] et al. [37] investigated automated feature detection, segmentation, and quantification of common findings by using DL-based computer vision techniques. The approaches based on the U-Net architecture showed the best results in medical applications. The overall best-performing architecture in the validation dataset was U-Net with DenseNet121. [PERSON] et al. [38], to distinguish various caries degrees from panoramic radiographs, suggested a unique deep learning architecture dubbed CariesNet. First, they gathered a high-quality panoramic radiograph dataset with 3127 clearly defined caries lesions that encompass shallow, intermediate, and deep caries. The model was trained using 304 radiographs and achieved a precision of 63.29%, recall of 65.02%, and F1-score of 64.14% on the test dataset. In addition, the diagnostic performance of all three clinicians improved significantly when they referred to the results of the CNN model.