Thus, it is indispensable to use digital radiographs during dental treatment. It is because of their location in the mineralized tissues (bone and teeth). Visual examination by dental care experts during dental treatment alone cannot provide sufficient information to diagnose a number of dental anomalies. Thus, the proposed model can be used as a useful and reliable tool to assist dental care professionals in dentistry. The result of tenfold CV effectively verifies the robustness of the model as the average F 1 score obtained is more than 0.970.
To test the feasibility and robustness of the model, a tenfold cross validation (CV) is presented in this paper. These results verify the proposed method’s ability to recognize teeth with high degree of accuracy. F 1 score improves from 0.978 to 0.982 for ResNet-101. The optimization technique further improves the results i.e. It achieves 0.974 and 0.981 mAPs for ResNet-50 and ResNet-101, respectively with faster R-CNN technique. The detection result obtained from faster R-CNN is further refined by using a candidate optimization technique that evaluates both positional relationship and confidence score of the candidates. An automatic teeth recognition model is proposed here using residual network-based faster R-CNN technique.
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