Automated classification of dual channel dental imaging of auto-fluorescence and white lightby convolutional neural networks

Abstract

Prevention is the most effective way to reduce dental caries. In order to provide a simple way to achieve oral healthcare direction in daily life, dual Channel, portable dental Imaging system that combine white light with autofluorescence techniques was established, and then, a group of volunteers were recruited, 7200 tooth pictures of different dental caries stage and dental plaque were taken and collected. In this work, a customized Convolutional Neural Networks (CNNs) have been designed to classify dental image with early stage caries and dental plaque. Eighty percentage (n=6000) of the pictures taken were used to supervised training of the CNNs based on the experienced dentists’ advice and the rest 20% (n=1200) were used to a test dataset to test the trained CNNs. The accuracy, sensitivity and specificity were calculated to evaluate performance of the CNNs. The accuracy for the early stage caries and dental plaque were 95.3% and 95.9%, respectively. These results shown that the designed image system combined the customized CNNs that could automatically and efficiently find early caries and dental plaque on occlusal, lingual and buccal surfaces. Therefore, this will provide a novel approach to dental caries prevention for everyone in daily life.

Keywords

• biomedical imaging
• caries
• tooth healthcare
• auto-flourescence
• automatic classification
• deep-learning