Facia-fix: mobile application for bell's palsy diagnosis and assessment using computer vision and deep learning.

Abstract

Facial paralysis (FP) is a condition characterized by the inability to move some or all of the muscles on one or both sides of the face. Diagnosing FP presents challenges due to the limitations of traditional methods, which are time-consuming, uncomfortable for patients, and require specialized clinicians. Additionally, more advanced tools are often uncommonly available to all healthcare providers. Early and accurate detection of FP is crucial, as timely intervention can prevent long-term complications and improve patient outcomes. To address these challenges, our research introduces Facia-Fix, a mobile application for Bell's palsy diagnosis, integrating computer vision and deep learning techniques to provide real-time analysis of facial landmarks. The classification algorithms are trained on the publicly available YouTube FP (YFP) dataset, which is labeled using the House-Brackmann (HB) method, a standardized system for assessing the severity of FP. Different deep learning models were employed to classify the FP severity, such as MobileNet, CNN, MLP, VGG16, and Vision Transformer. The MobileNet model which uses transfer learning, achieved the highest performance (Accuracy: 0.9812, Precision: 0.9753, Recall: 0.9727, F1 Score: 0.974), establishing it as the optimal choice among the evaluated models. The innovation of this approach lies in its use of advanced deep learning models to provide accurate, objective, non-invasive and real-time comprehensive quantitative assessment of FP severity. Preliminary results highlight the potential of Facia-Fix to significantly improve the diagnostic and follow-up experiences for both clinicians and patients.