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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F25%3A43927756" target="_blank" >RIV/00064173:_____/25:43927756 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11120/25:43927756 RIV/00216275:25530/25:39924132 RIV/60461373:22340/25:43930906

Výsledek na webu

<a href="https://doi.org/10.1016/j.bspc.2024.107152" target="_blank" >https://doi.org/10.1016/j.bspc.2024.107152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2024.107152" target="_blank" >10.1016/j.bspc.2024.107152</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;s exercise performance, thereby facilitating more precise outcomes of a classification.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;s exercise performance, thereby facilitating more precise outcomes of a classification.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30206 - Otorhinolaryngology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

101

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

107152

Kód UT WoS článku

001359144900001

EID výsledku v databázi Scopus

2-s2.0-85208937272