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Evaluating a neural network decision-support tool for the diagnosis of breast cancer

  • Joseph Downs
  • Robert F Harrison
  • Simon S Cross
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)

Abstract

This paper describes the evaluation of an application of the ARTMAP neural network model to the diagnosis of cancer from fine-needle aspirates of the breast. The network has previously demonstrated very high performance when used with high-quality data provided by an expert pathologist. New performance results are provided for its use with “noisy” data provided by an inexperienced pathologist. Additionally, ARTMAP supports the extraction of symbolic rules from a trained network and the validity of these autonomously-acquired rules is discussed. It is concluded that the symbolic rules provide an appropriate mapping of input features to category classes in the domain. However, the network in its present form is only suitable for use as a decision-support tool by a senior pathologist, since its performance deteriorated greatly with poor-quality data provided by a junior pathologist. The implications of the findings are discussed.

Keywords

Radial Basis Function Network Vote Strategy Rule Extraction Category Cluster Fine Needle Aspirate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Joseph Downs
    • 1
  • Robert F Harrison
    • 1
  • Simon S Cross
    • 2
  1. 1.Dept. of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
  2. 2.Dept. of PathologyUniversity of Sheffield Medical SchoolSheffieldUK

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