Decision support in screening mammography
Seán Hammond, IAN R. L. DAVIES, Paul T. Sowden, J.P. Davies · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
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.