Academic Journal of Computing & Information Science, 2024, 7(6); doi: 10.25236/AJCIS.2024.070615.
Lisha Bai, Yujian Li, Haojie Chang
School of Mechanical and Electronic Engineering, Wuhan University of Tecnology, Wuhan, China
With the development of computer-aided diagnosis, numerous studies have employed specialized algorithms to extract diagnostically valuable features from images, optimize the subsequent processing of these features, and finally perform classification evaluation on the processed features. This paper combines technologies such as facial recognition, image analysis and processing, and convolutional neural networks to design and implement a comprehensive facial nerve evaluation system. To comprehensively assess the severity of facial nerve disease in patients, this paper designs a Convolutional Neural Network (CNN) model for the extraction and grading of facial paralysis features. From data acquisition, preprocessing, data augmentation, model training to prediction and evaluation, a complete model for assessing and predicting the severity of facial paralysis in patients is established.
Classification Evaluation, Facial Paralysis Features, Neural Network Model
Lisha Bai, Yujian Li, Haojie Chang. Classification of Facial Paralysis Based on Machine Vision Techniques. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 6: 95-99. https://doi.org/10.25236/AJCIS.2024.070615.
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