DocumentCode :
296090
Title :
Improved recognition of E. coli 0157:H7 bacteria from pulsed-field gel electrophoresis images through the fusion of neural networks with fuzzy training data
Author :
Keller, James M. ; Wang, Dayou ; Carson, C. Andrew ; McAdoo, Kelly K. ; Bailey, Craig W.
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA
Volume :
4
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
1606
Abstract :
E. coli 0157:H7 is a particularly toxic strain of the E. coli bacteria group. Fast and accurate methodologies for recognizing this strain of bacteria are desired. In this paper, we demonstrate that the fusion of neural network outputs using (possibly redundant) banding pattern features from pulsed-field electrophoresis images, where the learning was done with fuzzy labeled training data, produced superior results over the single and crisp counterparts
Keywords :
biology computing; biomedical imaging; electrophoresis; fuzzy set theory; image recognition; neural nets; object recognition; sensor fusion; E. coli 0157:H7 bacteria; data fusion; fuzzy labeled training data; neural networks; pulsed-field gel electrophoresis images; recognition; Biochemistry; Biological cells; Capacitive sensors; DNA; Electrokinetics; Image recognition; Microorganisms; Neural networks; Pathogens; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.488858
Filename :
488858
Link To Document :
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