A Novel Framework for Detection of Facial Paralysis Using Cascaded Convolutional Neural Networks

Early detection and accurate diagnosis of facial paralysis are vital because of timely medical treatment and improved patient outcomes. Traditional diagnostic techniques are based on subjective evaluations, thus leading to unnecessary delays in diagnosis. This work attempts to solve this challenge by introducing a cascaded convolutional neural network (CNN) for the automatic diagnosis of facial paralysis signs from recorded facial images in real-time. Our proposed system uses advanced image preprocessing and feature extraction techniques to classify facial paralysis symptoms with great accuracy. The model was trained on a dataset composed of diverse facial expressions; it achieved a training accuracy of 98% and a testing accuracy of 99.86%. The cascaded CNN architecture is capable of detecting very effectively by combining many feature layers for correct classification. This system has enormous applicability in real-time telemedicine, remote diagnostics, and in continuous monitoring of patients. Thus, the project will tackle a relevant gap between advanced machine learning technology and health by providing a more scalable and efficient solution, accessible to many.