UGC Approved Journal no 63975(19)

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Published in:

Volume 9 Issue 10
October-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2210209


Registration ID:
503519

Page Number

c82-c99

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Title

Feature Point selection for facial paralysis, Classification of severity with neural network.

Abstract

Paralysis is a disease that affects the voluntary movements of muscles in the human body. Facial paralysis is a disease that affects the movements of muscles in the face on one side or both sides. Cranial nerves are responsible for various movements in the face and they originate from the brain and brain stem. Any damage in this cranial nerve system results in the loss of voluntary facial movements. The symptoms of facial paralysis are defacement of face during rest and certain expressions like drooping of eye, drooping of mouth corner, etc., during normal action. The patient affected by facial paralysis experiences difficulties in carrying out daily activities such as eating, drinking, and communicating and loss of taste and loss of hearing. It is necessary to assess the current level of severity of facial paralysis that has been acquired by the patient in order to treat the patient according to the severity of the disease. An accurate method for evaluating the disease will be an indispensable tool for the physician to choose appropriate treatment. The proposed research work focuses on assessment of the severity of facial paralysis. An accurate and quantitative evaluation of degree of facial paralysis is presented. This method evaluates the degree of facial paralysis on one side of face and classifies the severity of facial paralysis. The salient points are marked on both the sides of facial features in different facial expressions, and computation of overall degree evaluation values is carried out by an algorithm called Feature Point Selection Algorithm (FPSA). This algorithm comprises a sequence of the following steps: computation of distance between salient points on facial feature separately for left side and right side during each expression, selection of maximum and minimum distance from left and right side during each expression, computation of the difference between maximum distance of healthy side and sick side, also the difference between minimum distance of healthy side and sick side. The average of all difference of maximum distance and average of all difference of minimum distance are the overall degree evaluation values for a patient.

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"Feature Point selection for facial paralysis, Classification of severity with neural network.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.c82-c99, October-2022, Available :http://www.jetir.org/papers/JETIR2210209.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Feature Point selection for facial paralysis, Classification of severity with neural network.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppc82-c99, October-2022, Available at : http://www.jetir.org/papers/JETIR2210209.pdf

Publication Details

Published Paper ID: JETIR2210209
Registration ID: 503519
Published In: Volume 9 | Issue 10 | Year October-2022
DOI (Digital Object Identifier):
Page No: c82-c99
Country: Kota, Rajasthan, India .
Area: Medical Science
ISSN Number: 2349-5162
Publisher: IJ Publication


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