Multi Path Heterogeneous Neural Networks: Novel comprehensive classification method of facial nerve function

This paper introduces a systematic classification of the facial nerve grading system using a comprehensive methodology using a pioneering Multi-Path Heterogeneous Neural Network (MPHNN) method designed for the accurate classification of exercise. It integrates four distinct Convolutional Neural Networks (CNNs) and Custom Feedforward Neural Networks (CFNNs) to enhance the precision of the classification. The CNNs are specifically tailored to scrutinize changes in the coordinates of facial landmarks over time, enabling the capture of both spatial information and temporal patterns in facial expressions during exercise. The CFNNs incorporate patient-specific variables and exercise statistics, including factors such as their surgical history, the type of exercise, its duration, and synthetic features like cumulative movement for each landmark. By leveraging this comprehensive framework, the proposed method offers a nuanced representation of the patient’s exercise performance, thereby facilitating more precise outcomes of a classification. • Multi-Path Heterogeneous Neural Network method designed for accurate classification of exercises. • Convolutional Neural Networks and Custom Feedforward Neural Networks to enhance classification precision. • Facilitates more precise classification outcomes.