Deep learning for classification of dental plaque images

Abstract

Dental diseases such as caries or gum disease are caused by prolonged exposure to pathogenic plaque. Assessment of such plaque accumulation can be used to identify individuals at risk. In this work we present an automated dental red autofluorescence plaque image classification model based on application of Convolutional Neural Networks (CNN) on Quantitative Light-induced Fluorescence (QLF) images. CNN model outperforms other state of the art classification models providing a 0.75 ± 0.05 F1-score on test dataset. The model directly benefits from multi-channel representation of the images resulting in improved performance when all three colour channels were used.