Gingivitis Classification via Wavelet Entropy and Support Vector Machine

Abstract

Gingivitis is usually detected by a series of oral examinations. In this process, the dental record plays a very important role. However, it often takes a lot of physical and mental effort to accurately detect gingivitis in a large number of dental records. Therefore, it is of great significance to study the classification technology of gingivitis. In this study, a new gingivitis classification method based on wavelet entropy and support vector machine is proposed to help diagnose gingivitis. The feature of the image is extracted by wavelet entropy, and then the image is classified by support vector machine. The experimental results show that the average sensitivity, specificity, precision and accuracy of this method are 75.17%, 75.29%, 75.35% and 75.24% respectively, which are superior to the other three methods This method is proved to be effective in the classification of gingivitis.