Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi

Abstract

Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP.

Computer vision for eye diseases detection using pre‐trained deep learning techniques and raspberry Pi

“…The Raspberry Pi 4 Model B, which is a popular microcomputer single-board type, worked as the core for the proposed hardware system used to provide computational power, the required connectivity for real-time imaging processes, and finally, the interface of deep learning. In addition, this type of Raspberry Pi 4 Model B was chosen for the processing capabilities of enhancement, the ability to improve performance and the highest memory capacity [24] compared to other Raspberry Pi models. The touch screen has been integrated into the proposed system of specification represented by WaveShare HDMI Screen, and 5-inch with 800 × 480 pixel resolution.…”

BioMedInformatics, the Link between Biomedical Informatics, Biology and Computational Medicine

“…Saber Amsalam and colleagues solved a complex problem in Iraq by automatically detecting facial paralysis, age and gender using a Raspberry Pi [29]. They proposed a complete recognition and detection system with a small portable system.…”