Preliminary Study of Dental Caries Detection by Deep Neural Network Applying Domain-Specific Transfer Learning

Abstract

Abstract Purpose The purpose of this study is to confirm whether it is possible to acquire a certain degree of diagnostic ability even with a small dataset using domain-specific transfer learning. In this study, we constructed a simulated caries detection model on panoramic tomography using transfer learning. Methods A simulated caries model was trained and validated using 1094 trimmed intraoral images. A convolutional neural network (CNN) with three convolution and three max pooling layers was developed. We applied this caries detection model to 50 panoramic images and evaluated its diagnostic performance. Results The diagnostic performance of the CNN model on the intraoral film was as follows: C0 84.6%; C1 90.6%; C2 88.6%. Finally, we tested 50 panoramic images with simulated caries insertion. The diagnostic performance of the CNN model on the panoramic image was as follows: C0 75.0%, C1 80.0%, C2 80.0%, and overall diagnostic accuracy was 78.0%. The diagnostic performance of the caries detection model constructed only with panoramic images was much lower than that of the intraoral film. Conclusion Domain-specific transfer learning methods may be useful for saving datasets and training time (179/250).