The reliability issue of computer-aided breast cancer diagnosis

Boris Kovalerchuk, Evangelos Triantaphyllou, James F. Ruiz, Vetle I. Torvik, Evgeni Vityaev

Research output: Contribution to journalArticlepeer-review


This paper introduces a number of reliability criteria for computer-aided diagnostic systems for breast cancer. These criteria are then used to analyze some published neural network systems. It is also shown that the property of monotonicity for the data is rather natural in this medical domain, and it has the potential to significantly improve the reliability of breast cancer diagnosis while maintaining a general representation power. A central part of this paper is devoted to the representation/narrow vicinity hypothesis, upon which existing computer-aided diagnostic methods heavily rely. The paper also develops a framework for determining the validity of this hypothesis. The same framework can be used to construct a diagnostic procedure with improved reliability. (C) 2000 Academic Press.

Original languageEnglish (US)
Pages (from-to)296-313
Number of pages18
JournalComputers and Biomedical Research
Issue number4
StatePublished - 2000
Externally publishedYes


  • Computer-aided diagnostic systems
  • Data monotonicity
  • Discriminant analysis
  • Machine learning
  • Neural networks
  • Reliability of diagnosis
  • Representation/narrow vicinity hypothesis

ASJC Scopus subject areas

  • Medicine (miscellaneous)


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