Computer Vision System for Facial Palsy Detection

Abstract

Facial palsy (FP) is a disorder that affects the seventh facial nerve, which makes the patient unable to control facial movements and expressions with other vital activities. It affects one side of the face, and it is usually diagnosed by the asymmetry of the two sides of the face through visual inspection by a doctor. However, the visual inspection is human-based, which is prone to errors because the doctor is exposed to omission due to fatigue and work stress. Therefore, it is important to develop a new method for detecting FP through artificial intelligence and use a more accurate computerized system to reduce the effort and cost of patients and increase the accuracy of diagnosis. This work  aims to establish a safe, useful and high-accuracy diagnostic system for FP that can be used by the patient and proposes to detect FP using a digital camera and deep learning techniques automatically. The system could be used by the patient himself at home without needing to visit the hospital. The proposed system trained 570 images, including 200 images of FP palsy. The proposed FP system achieved an accuracy of 98%. This confirms the effectiveness of the proposed system and makes it an efficient medical examination tool for detecting FP.