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

Publication ,  Journal Article
Tourassi, GD; Markey, MK; Lo, JY; Floyd, CE
Published in: Med Phys
May 2001

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.

Duke Scholars

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

May 2001

Volume

28

Issue

5

Start / End Page

804 / 811

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Models, Statistical
  • Middle Aged
  • Humans
  • Female
  • Databases, Factual
  • Breast Neoplasms
  • Algorithms
  • Aged
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Tourassi, G. D., Markey, M. K., Lo, J. Y., & Floyd, C. E. (2001). A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys, 28(5), 804–811. https://doi.org/10.1118/1.1367861
Tourassi, G. D., M. K. Markey, J. Y. Lo, and C. E. Floyd. “A neural network approach to breast cancer diagnosis as a constraint satisfaction problem.Med Phys 28, no. 5 (May 2001): 804–11. https://doi.org/10.1118/1.1367861.
Tourassi GD, Markey MK, Lo JY, Floyd CE. A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys. 2001 May;28(5):804–11.
Tourassi, G. D., et al. “A neural network approach to breast cancer diagnosis as a constraint satisfaction problem.Med Phys, vol. 28, no. 5, May 2001, pp. 804–11. Pubmed, doi:10.1118/1.1367861.
Tourassi GD, Markey MK, Lo JY, Floyd CE. A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys. 2001 May;28(5):804–811.

Published In

Med Phys

DOI

ISSN

0094-2405

Publication Date

May 2001

Volume

28

Issue

5

Start / End Page

804 / 811

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Models, Statistical
  • Middle Aged
  • Humans
  • Female
  • Databases, Factual
  • Breast Neoplasms
  • Algorithms
  • Aged