PDDNet: Deep Learning Based Dental Disease Classification through Panoramic Radiograph Images

The high prevalence of dental cavities is a global public health concern. If untreated, cavities can lead to tooth loss, but timely detection and treatment can prevent this outcome. X-ray imaging provides crucial insights into the structure of teeth and surrounding tissues, enabling dentists to identify issues that may not be immediately visible. However, manual assessment of dental X-rays is time-consuming and prone to errors due to variations in dental structures and limited expertise. Automated analysis technology can reduce dentists’ workload and improve diagnostic accuracy. This study proposes the Prediction of Dental Disease Network (PDDNet), a CNN-based model for classifying three categories of dental disease: cavities, fillings, and implants, using X-ray images. PDDNet’s performance is compared with six well-known deep CNN classifiers: DenseNet-201, Xception, ResNet50V2, Inception-V3, Vgg-19, and EfficientNet-B0. To ensure balanced class distribution and enhance classification accuracy, the ADASYN oversampling technique is employed. PDDNet achieves an impressive accuracy of 99.19%, recall of 99.19%, precision of 99.19%, AUC of 99.97%, and F1-score of 99.17%, outperforming the other classifiers across multiple performance metrics. These findings demonstrate PDDNet’s potential to provide significant assistance to dental professionals in diagnosing dental diseases.