A neural network approach to breast cancer diagnosis as a constraint satisfaction problem.


Journal Article

A constraint satisfaction neural network (CSNN) approach is proposed for breast cancer diagnosis using mammographic and patient history findings. Initially, the diagnostic decision to biopsy was formulated as a constraint satisfaction problem. Then, an associative memory type neural network was applied to solve the problem. The proposed network has a flexible, nonhierarchical architecture that allows it to operate not only as a predictive tool but also as an analysis tool for knowledge discovery of association rules. The CSNN was developed and evaluated using a database of 500 nonpalpable breast lesions with definitive histopathological diagnosis. The CSNN diagnostic performance was evaluated using receiver operating characteristic analysis (ROC). The results of the study showed that the CSNN ROC area index was 0.84+/-0.02. The CSNN predictive performance is competitive with that achieved by experienced radiologists and backpropagation artificial neural networks (BP-ANNs) presented before. Furthermore, the study illustrates how CSNN can be used as a knowledge discovery tool overcoming some of the well-known limitations of BP-ANNs.

Full Text

Duke Authors

Cited Authors

  • Tourassi, GD; Markey, MK; Lo, JY; Floyd, CE

Published Date

  • May 2001

Published In

Volume / Issue

  • 28 / 5

Start / End Page

  • 804 - 811

PubMed ID

  • 11393476

Pubmed Central ID

  • 11393476

International Standard Serial Number (ISSN)

  • 0094-2405

Digital Object Identifier (DOI)

  • 10.1118/1.1367861


  • eng

Conference Location

  • United States