A Comparative Study of MLP and RBF Neural Nets in the Estimation of the Foetal Weight and Length
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چکیده
Foetal weight estimation is a clinically relevant task for proper medical care in perinatal situations. Usually this estimation is based on features such as measurements derived from echographic examinations. Several formulas have been developed by other authors for performing this estimation with limited degree of success. Our approach is based on multilayer perceptrons (MLP) and radial basis functions (RBF) neural nets in order to achieve a clinically usable estimation of foetal weight. In this paper we report optimistic results by training the MLP using the fast Levenberg-Marquadt algorithm, and the RBF by using the EM (ExpectationMaximization) algorithm. The performance of the two architectures is compared and the results show significant improvements over the formulas
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تاریخ انتشار 2000