Periodontal Disease Classification with Color Teeth Images Using Convolutional Neural Networks
Saron Park, Habibilloh Erkinov, Md. Al Mehedi Hasan, Seoul‐Hee Nam, Yu‐Rin Kim, Jungpil Shin, Won-Du Chang
Abstract
Oral health plays an important role in people’s quality of life as it is related to eating, talking, and smiling. In recent years, many studies have utilized artificial intelligence for oral health care. Many studies have been published on tooth identification or recognition of dental diseases using X-ray images, but studies with RGB images are rarely found. In this paper, we propose a deep convolutional neural network (CNN) model that classifies teeth with periodontal diseases from optical color images captured in front of the mouth. A novel network module with one-dimensional convolutions in parallel was proposed and compared to the conventional models including ResNet152. In results, the proposed model achieved 11.45% higher than ResNet152 model, and it was proved that the proposed structure enhanced the training performances, especially when the amount of training data was insufficient. This paper shows the possibility of utilizing optical color images for the detection of periodontal diseases, which may lead to a mobile oral healthcare system in the future.