• DocumentCode
    2860564
  • Title

    Neural network paradigm comparisons for appendicitis diagnoses

  • Author

    Eberhart, R.C. ; Dobbins, R.W. ; Hutton, L.V.

  • Author_Institution
    Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
  • fYear
    1991
  • fDate
    12-14 May 1991
  • Firstpage
    298
  • Lastpage
    304
  • Abstract
    The results of comparisons among diagnoses of appendicitis versus nonspecific abdominal pain using three neural-network paradigms are reported. The paradigms used were the back propagation, binary adaptive resonance theory, and fuzzy resonance paradigms. It appears, from the limited testing done, that the back-propagation network performs best. Also discussed is the need to standardize input data files to facilitate paradigm comparisons and minimize software system development time. A structure for network input data files that could contribute to a process of standardization is proposed. The work is part of an effort to develop a medical practice support system to be used in isolated environments such as submarines
  • Keywords
    adaptive systems; medical diagnostic computing; neural nets; abdominal pain; appendicitis diagnoses; back propagation; binary adaptive resonance theory; fuzzy resonance paradigms; input data files; isolated environments; medical practice support system; network input data files; neural-network paradigms; software system development time; Abdomen; Back; Medical diagnostic imaging; Neural networks; Pain; Performance evaluation; Resonance; Software systems; Standardization; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer-Based Medical Systems, 1991. Proceedings of the Fourth Annual IEEE Symposium
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-8186-2164-8
  • Type

    conf

  • DOI
    10.1109/CBMS.1991.128983
  • Filename
    128983