A Novel Approach for Dental X-Ray Enhancement and Caries Detection

Typical manual processes are time-consuming, error-prone, and subjective, especially for complex radiological diagnoses. Although current artificial intelligence models show promising results for identifying caries, they generally fail due to a lack of well-pre-processed images. This research work is two-fold. Initially, we propose a novel layer division non-zero elimination model to reduce Poisson noise and de-blur the acquired images. In the second step, we propose a more accurate and intuitive method in segmenting and classifying caries of the teeth. We used a total of 17 840 radiographs, which are a mix of bitewing and periapical X-rays, for classification with ResNet-50 and segmentation with ResUNet. ResNet-50 uses skip connections within the residual blocks to solve the gradient issue existing in cavity presence. ResUNet combines the encoder-decoder structure of U-Net with the residual block features of ResNet to improve the performance of segmentation on radiographs with cavities. Finally, the Stochastic Gradient Descent optimizer was employed during the training phase to ensure the possibility of convergence and improve accuracy. ResNet-50 was proven to outperform earlier versions, like ResNet-18 and ResNet-34, in achieving a recognition accuracy of 87% in the classification challenge, which is a very reliable indicator of promising results. Similarly, ResUNet was proved to be better than existing state-of-the-art models such as CariesNet, DeepLab v3, and U-Net++ in terms of accuracy, even achieving the level of 98% accuracy in segmentation.