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	Breast cancer diagnosis using neural-based linear fusion strategies
	WU, YF ; WANG, C ; NG, SC ; MADABHUSHI, A ; ZHONG, YX ; Wu YF(吴云峰)
	2006
关键词	NETWORKS
英文摘要	Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, which are used to combine a group of component multilayer perceptrons with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perceptron Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70751]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	WU, YF,WANG, C,NG, SC,et al. Breast cancer diagnosis using neural-based linear fusion strategies[J],2006.
APA	WU, YF,WANG, C,NG, SC,MADABHUSHI, A,ZHONG, YX,&吴云峰.(2006).Breast cancer diagnosis using neural-based linear fusion strategies..
MLA	WU, YF,et al."Breast cancer diagnosis using neural-based linear fusion strategies".(2006).

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