Study on Facial Paralysis Recognition Method with SOLOv2-Vision Transformer
10.14148/j.issn.1672-0482.2025.1399
- VernacularTitle:融合SOLOv2-Vision Transformer的面瘫识别方法研究
- Author:
Zhelong ZHUANG
1
;
Youwei DING
;
Kongfa HU
;
Kehong CHEN
;
Gong CHEN
Author Information
1. 南京中医药大学人工智能与信息技术学院,江苏 南京 210023
- Publication Type:Journal Article
- Keywords:
image segmentation;
image classification;
attention mechanism;
facial paralysis;
diagnosis;
SOLOv2-Vision Trans-former
- From:
Journal of Nanjing University of Traditional Chinese Medicine
2025;41(10):1399-1406
- CountryChina
- Language:Chinese
-
Abstract:
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 seg-mented by the SOLOv2 model with a replaced backbone network,the interference part in the image was removed,and then inputted in-to 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-Vi-sion Transformer can achieve higher recognition accuracy than the unsegmented method,and provides a new method for the diagnosis of facial paralysis.
