Mouth Cavity Visual Analysis Based on Deep Learning for Oropharyngeal Swab Robot Sampling

Abstract

The visual analysis of the mouth cavity plays a significant role in the pathogen specimen sampling and disease diagnosis of the mouth cavity. Aiming at performance defects of general detectors based on deep learning in detecting mouth cavity components, this article proposes a mouth cavity analysis network (MCNet), which is an instance segmentation method with spatial features, and a mouth cavity dataset (MCData), which is the first available dataset for mouth cavity detecting and segmentation. First, given the lack of a mouth cavity image dataset, the MCData for detecting and segmenting key parts in the mouth cavity was developed for model training and testing. Second, the MCNet was designed based on the mask region-based convolutional neural network. To improve the performance of feature extraction, a parallel multiattention module was designed. Besides, to solve low detection accuracy of small-sized objects, a multiscale region proposal network structure was designed. Then, the mouth cavity spatial structure features were introduced, and the detection confidence could be refined to increase the detection accuracy. The MCNet achieved 81.5% detection accuracy and 78.1% segmentation accuracy (intersection over union = 0.50:0.95) on the MCData. Comparative experiments with the MCData showed that the proposed MCNet outperformed state-of-the-art approaches with the task of mouth cavity instance segmentation. In addition, the MCNet has been used in an oropharyngeal swab robot for COVID-19 oropharyngeal sampling.