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A neural network approach to breast cancer diagnosis as a constraint satisfaction problem
Author(s) -
Tourassi Georgia D.,
Markey Mia K.,
Lo Joseph Y.,
Floyd Carey E.
Publication year - 2001
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1367861
Subject(s) - artificial neural network , backpropagation , computer science , artificial intelligence , machine learning , receiver operating characteristic , breast cancer , constraint (computer aided design) , constraint satisfaction problem , mammography , data mining , pattern recognition (psychology) , cancer , medicine , mathematics , geometry , probabilistic logic
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.