  Images are an important data source for diagnosis and treatment of oral
diseases. The manual classification of images may lead to misdiagnosis or
mistreatment due to subjective errors. In this paper an image classification
model based on Convolutional Neural Network is applied to Quantitative
Light-induced Fluorescence images. The deep neural network outperforms other
state of the art shallow classification models in predicting labels derived
from three different dental plaque assessment scores. The model directly
benefits from multi-channel representation of the images resulting in improved
performance when, besides the Red colour channel, additional Green and Blue
colour channels are used.
