Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Biomedical Signal Processing and Control

DOI: 10.1016/j.bspc.2024.107152

Authors: Alan Spark, Jan Kohout, Ludmila Verešpejová, Martin Chovanec, Jan Mareš

Institutions: University of Chemistry and Technology, Prague, Charles University, University Hospital Kralovske Vinohrady, University of Pardubice

Language: English

Topic: Facial Nerve Paralysis Treatment and Research

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,510