Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network
This work addresses misdiagnosis in oral disease treatment by automating image classification, but it is incremental as it applies an existing CNN method to a specific dental imaging domain.
The paper tackled the problem of subjective errors in manual classification of Quantitative Light-Induced Fluorescence images for dental plaque assessment by applying a Convolutional Neural Network, which outperformed other state-of-the-art shallow models and improved performance using multi-channel image representation.
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.