Optimizing hidden layer node number of BP network to estimate fetal weight

Juan Su; Yuanwen Zou; Jiangli Lin; Tianfu Wang; Deyu Li; Tao Xie

doi:10.1117/12.750383

14 November 2007 Optimizing hidden layer node number of BP network to estimate fetal weight

Juan Su, Yuanwen Zou, Jiangli Lin, Tianfu Wang, Deyu Li, Tao Xie

Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 678914 (2007) https://doi.org/10.1117/12.750383
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the hidden layer node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the hidden layer node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Citation Download Citation

Juan Su, Yuanwen Zou, Jiangli Lin, Tianfu Wang, Deyu Li, and Tao Xie "Optimizing hidden layer node number of BP network to estimate fetal weight", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 678914 (14 November 2007); https://doi.org/10.1117/12.750383

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