Abstract
Popularizing community and home early caries screening is essential for caries prevention and treatment. However, a high-precision, low-cost, and portable automated screening tool is currently lacking. This study constructed an automated diagnosis model for dental caries and calculus using fluorescence sub-band imaging combined with deep learning. The proposed method is divided into two stages: the first stage collects imaging information of dental caries in different fluorescence spectral bands and obtains six-channel fluorescence images. The second stage employs a 2-D-3-D hybrid convolutional neural network combined with the attention mechanism for classification and diagnosis. The experiments demonstrate that the method has competitive performance compared to existing methods. In addition, the feasibility of transferring this approach to different smartphones is discussed. This highly accurate, low-cost, portable method has potential applications in community and at-home caries detection.