A constraint satisfaction neural network for medical diagnosis

Author

Tourassi, Georgia D. ; Floyd, Carey E., Jr. ; Lo, Joseph Y.

Author_Institution

Duke Univ. Med. Center, Durham, NC, USA

Volume

5

fYear

1999

fDate

1999

Firstpage

3632

Abstract

The objective of this study was to explore how a constraint satisfaction neural network (CSNN) can be used for medical diagnostic tasks. The study is based on a database of 500 patients who underwent breast biopsy at Duke University Medical Center due to suspicious mammographic findings. A CSNN was developed and evaluated to predict the biopsy result from the patient´s mammographic findings. The diagnostic performance of the CSNN network was compared to a traditional backpropagation neural network and a case-based-reasoning algorithm by means of receiver operating characteristics analysis. The study demonstrates (i) how CSNNs can be applied to medical diagnostic tasks and, (ii) how they can be utilized to extract meaningful clinical information regarding underlying relationships among medical findings and associated diagnoses

Keywords

cancer; mammography; medical diagnostic computing; neural nets; backpropagation neural network; breast biopsy; case-based-reasoning algorithm; constraint satisfaction neural network; meaningful clinical information; medical diagnosis; receiver operating characteristics analysis; Algorithm design and analysis; Backpropagation algorithms; Breast biopsy; Clinical diagnosis; Data mining; Databases; Medical diagnosis; Medical diagnostic imaging; Neural networks; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.836258

Filename

836258