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