In this thesis, an anomaly detection algorithm is applied to a mobile oral health care application. Particularly, one class YOLOv3 has been investigated as an anomaly detection model to classify pictures of mouths, which will serve as inputs for subsequent machine learning models. Outstanding performance has been achieved by proposing appropriate annotation strategies for the datasets and modifying the loss function. Notably, the model can classify not only oral and non-oral pictures but also output preprocessed pictures that only contain the area around the lips by using the predicted bounding box. Thus, the model performs both prediction and preprocessing simultaneously.
