Automatic identifying laryngopharyngeal reflux using artificial neural network

Sheng-Fuu Lin*, Hsien Tse Chen, Tung Lung Tsai

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Laryngopharyngeal Reflux mostly leads to burns of the pharynx and larynx by reflux from gastric acid and also leads to different degrees of burns in the esophagus and the stomach. This paper aims to develop a technique for analyzing pharyngeal and laryngeal images. With techniques of digital image processing, this paper can choose the suitable images from burns of the pharynx and larynx to obtain the feature zones of burns of the pharynx and larynx. Artificial neural network helps physicians to develop the diagnostic standard about the burns severity of Laryngopharyngeal Reflux. This paper divides the types of the complications into three degrees and compares with other ways (Hanson et al.5 and Ilgner et al.6). The results can be the technical assistance in helping physicians to diagnose the severity of Laryngopharyngeal Reflux and to make a more precise diagnosis.

Original languageEnglish
Pages (from-to)47-56
Number of pages10
JournalBiomedical Engineering - Applications, Basis and Communications
Volume24
Issue number1
DOIs
StatePublished - 1 Jan 2012

Keywords

  • Artificial neural network
  • Feature extraction
  • Image processing
  • Laryngopharyngeal Reflux

Fingerprint Dive into the research topics of 'Automatic identifying laryngopharyngeal reflux using artificial neural network'. Together they form a unique fingerprint.

Cite this