Gingivitis detection by Fractional Fourier Entropy and Biogeography-based Optimization

As people keep a watch eye on the oral health, more people choose to go to professional dental hospitals for the regular dental examinations and diagnosis. It is well known that the dental diagnosis and treatment require excellent nursing skills and extensive experience by the dentists. Nervously, the number of experts is limited. However, the rapid increase in the number of diagnoses and the small number of professional dentists resulted in an increase in the daily diagnostic frequency of dentists, and the overworked working hours seriously affected the energy and diagnostic efficiency of dentists. This study for the sake of reduce the burden of dental diagnosis, proposes a computer-aided diagnosis method. This method classifies gingivitis images by using the image feature extraction method of fractional Fourier entropy (FRFE) and biogeography-based optimization (BBO) algorithm. The FRFE coefficient extracted from the image was used as the input feature vector, and the classification was carried out by the BBO algorithm with the optimal scheme of automatic screening. After 10-fold cross-validation, more effective healthy and pathological gingival image classification results were obtained compared with the latest methods.