• 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