Automatic diagnosis of dental diseases using convolutional neural network and panoramic radiographic images

Abstract

ABSTRACT People may experience tooth decay many times in a lifetime; however, its early diagnosis can greatly help accelerate the treatment process. Intelligent methods are now widely used in medicine, especially for diagnosis through panoramic radiographs. However, dental diseases have received little research focus due to the lack of a standard dataset. This study aimed to collect a standard dataset of panoramic radiographs of jaws and teeth. A deep neural network algorithm was then employed to classify teeth as healthy, decayed, root-canaled, and restored categories. The dataset was divided into five groups to analyse the proposed method in performance. Different evaluation criteria, confusion matrices, and precision percentages were determined for each group. On average, the final precision was 92% in the five groups. Moreover, the trained network results were compared with those of AlexNet and VGGNet16. Six months were spent collecting two new datasets labelled by experts. A smart method was also proposed to determine the dental location of jaws and reach dental diagnosis. According to the final results, the proposed network proved to be highly stable and managed to establish a better dental diagnosis.