Title :
The classification of the depth of burn injury using hybrid neural network
Author :
Zhao, Sean X. ; Lu, Taiwei
Author_Institution :
R&D Div., Phys. Opt. Corp., Torrance, CA, USA
Abstract :
This paper reports on a preliminary study of the classification of burn injuries using a neural network enhanced spectrometer system. Each burn injury is classified as superficial or full-thickness. Spectra covering the visible and near infrared range were collected from burn areas and subjected to autoscaling, principal component analysis, signal preprocessing, and pattern recognition. Classification of 56 data sets collected by the University of Washington Burn Center by this method showed 87.5% classification accuracy
Keywords :
backpropagation; biomedical measurement; infrared spectroscopy; medical signal processing; multilayer perceptrons; pattern classification; pattern recognition; skin; visible spectroscopy; 56 data sets; University of Washington Burn Center; autoscaling; burn areas; burn injury depth; classification; classification accuracy; full-thickness burn injury; hybrid neural network; near infrared range; neural network enhanced spectrometer system; pattern recognition; principal component analysis; signal preprocessing; spectra; superficial burn injury; visible range; Infrared spectra; Injuries; Multi-layer neural network; Neural networks; Neurons; Pattern recognition; Physical optics; Principal component analysis; Skin; Testing;
Conference_Titel :
Engineering in Medicine and Biology Society, 1995., IEEE 17th Annual Conference
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-2475-7
DOI :
10.1109/IEMBS.1995.575377
