Abstract Early diagnosis of dental caries has become increasingly important in recent years. It reduces irreversible tooth loss, treatment costs and treatment time. However, since the examination of dental caries is carried out visually by experts on radiographic images, the analysis process is quite exhausting for the experts. In addition, visual analysis may miss early-stage caries due to the workload in the clinical environment. In this study, an automatic caries diagnosis system is proposed to support the expert and to reduce the clinical workload by using panoramic images. The proposed DenseNet121-C model, based on deep learning models, generates results with its configured classifier for caries detection. The dataset prepared for the study includes 14498 tooth images automatically cropped from panoramic images. The proposed model achieved the highest performance on the test set with 93.17% accuracy, 89.43% precision, 85.84% recall, and 87.49% F1-score. Considering the high results of the current study, dentists can spend more time on treatment during dental examinations, thanks to the model’s ability to distinguish between caries and non-caries teeth. The results obtained were compared with the Mask R-CNN results. In addition, the performance of the deep learning architectures was investigated on an unbalanced dataset.
Ozdemir et al. (Thu,) studied this question.