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Proceedings Paper

Decision support in screening mammography
Author(s): Sean M. Hammond; Ian R. L. Davies; Paul T. Sowden; Jason Davies
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Paper Abstract

We are developing a statistical decision support system for use in screening mammography, and here we report on the rationale underlying its design, and on some preliminary tests of the system. A single expert radiologist described 200 mammograms, with known outcome, in terms of 38 critical features. We then compared discriminant function analysis (DFA), logistic regression (LR) and a backpropagation neural network (BNN) on their performance in classifying the 200 mammograms as normal or abnormal. All three approaches achieved greater than 90% correct classification, but DFA had low sensitivity and LR had a 9% miss rate, whereas the BNN detected all the cancers. External evaluation of LR and BNN on a new set of 167 mammograms showed that specificity was still high (greater than 96%) but sensitivity was less than 85%. We propose developing a system combining LR and BNN.

Paper Details

Date Published: 27 March 1996
PDF: 5 pages
Proc. SPIE 2712, Medical Imaging 1996: Image Perception, (27 March 1996); doi: 10.1117/12.236853
Show Author Affiliations
Sean M. Hammond, Univ. of Surrey (United Kingdom)
Ian R. L. Davies, Univ. of Surrey (United Kingdom)
Paul T. Sowden, Univ. of Surrey (United Kingdom)
Jason Davies, Univ. of Surrey (United Kingdom)


Published in SPIE Proceedings Vol. 2712:
Medical Imaging 1996: Image Perception
Harold L. Kundel, Editor(s)

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