Facial Image Classification for Obstructive Sleep Apnea Pre-Screening

In order to effectively implement Obstructive Sleep Apnea (OSA) pre-screening, an OSA pre-recognition model based on Resnet50 network was proposed. The frontal and lateral faces of 1000 patients with OSA were collected, and a series of preprocessing operations were performed to augment the image data, which were then fed into the Resnet50 network. The experimental results show that compared with the frontal face data, the classification accuracy obtained by using the side face data input into the network is 17.4% higher on average, and the frontal and lateral face data were indeed helpful in the pre - screening of obstructive sleep apnea. The grad-cam method shows that the focus areas of the model presented in this paper basically coincide with the face areas diagnosed in medical clinic.