DocumentCode :
276573
Title :
Neural networks for second-order medical tasks
Author :
Brown, David G.
Author_Institution :
FDA, Rockville, MD, USA
Volume :
i
fYear :
1991
fDate :
8-14 Jul 1991
Firstpage :
195
Abstract :
The ability of a neural network with a sigmoid output-node threshold function to simulate a purely quadratic decision function was studied. The network was applied to the diagnosis of diffuse lung disease. It was found that a minimally configured network can achieve this goal. The convergence of the network is graphically presented, and its performance was normalized to that of the ideal Bayesian decision maker. In a preliminary application, it easily distinguished between normal and pneumonic regions of the lung
Keywords :
Bayes methods; convergence; decision support systems; decision theory; lung; medical diagnostic computing; neural nets; convergence; diagnosis; diffuse lung disease; ideal Bayesian decision maker; minimally configured network; neural network; pneumonic regions; quadratic decision function; second-order medical tasks; sigmoid output-node threshold function; Bayesian methods; Biomedical imaging; Diseases; Feedforward neural networks; Feedforward systems; Lungs; Medical diagnostic imaging; Medical simulation; Neural networks; Pixel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
Type :
conf
DOI :
10.1109/IJCNN.1991.155175
Filename :
155175
Link To Document :
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