Profiling bell's palsy based on House-Brackmann score

In this study, we propose to examine facial nerve palsy using Support Vector Machines (SVMs) and Emergent Self-Organizing Map (ESOM). This research seeks to analyze facial palsy domain using facial features and grade the degree of nerve damage according to House-Brackmann score. Traditional evaluation methods involve a medical doctor taking a thorough history of a patient and determines the onset of the paralysis, the rate of progression and etc. The most important step is to assess the degree of voluntary movement present and document the grade of facial paralysis using House-Brackmann score. The significance of this work is that we attempt to apprehend this grading using semi-supervised learning with the aim of automating this grading process. The value of this research stems from the fact that there is a lack of literature seen in this area. The use of automated grading system greatly reduces assessment time and increases consistency because references of all palsy images are stored to provide references and comparison. The proposed automated diagnostics methods are computationally efficient making them ideal for remote assessment of facial palsy, profiling of a large number of facial images captured using mobile phones and digital cameras.