Title :
The application of improved BP neural network in the diagnosis of breast tumors
Author :
Liu, Ming ; Dong, Xiaogang
Author_Institution :
Coll. of Basic Sci., Changchun Univ. of Technol., Changchun, China
Abstract :
The traditional BP neural network is improved and developed in this paper. When the iteration of Levenberg-Marquardt takes the place of Gradient descent algorithm, the network convergence rate is improved greatly. After the analysis of breast tumors offered by Dr. William H. Wolberg from University of Wisconsin Hospitals, 9 parameters reflecting the characteristics of breast tumors are concluded. Based on the improved BP neural network, the simulation model of breast tumors is founded. Within the 83 groups of testing data, benign diagnosis rate is 100%, while malign diagnosis rate is 96.6%.
Keywords :
backpropagation; gradient methods; iterative methods; medical computing; neural nets; patient diagnosis; BP neural network improvement; Levenberg-Marquardt iteration; breast tumor diagnosis; gradient descent algorithm; network convergence rate; simulation model; Biological neural networks; Breast cancer; Breast tumors; Educational institutions; Testing; BP Neural Network; breast tumors; iteration of L-M; weight;
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
DOI :
10.1109/ICSAI.2012.6223260
