OBJECTIVE To establish an accurate and timely intelligent auxiliary diagnosis method for facial paralysis in order to enable patients and doctors to diagnose the disease faster and achieve the purpose of early detection, early diagnosis and early treatment.

METHODS A method integrating SOLOv2-Vision Transformer was proposed. The collected facial paralysis data was segmented by the SOLOv2 model with a replaced backbone network, the interference part in the image was removed, and then inputted into the Vision Transformer model for classification training. By adopting the principle of segmentation first and then classification, the classification effect of facial paralysis images was improved.

RESULTS The accuracy rate of the experimental method on the MEEI facial paralysis dataset was 0.982, the recall rate was 0.982, and the F1-score was 0.981, which were respectively increased by 2%, 4%, and 4% compared with the basic model.

CONCLUSION The facial paralysis classification model integrated with SOLOv2-Vision Transformer can achieve higher recognition accuracy than the unsegmented method, and provides a new method for the diagnosis of facial paralysis.