Abstract：On the basis of classical theory about spectral analysis, the present article used the method of principal component analysis to get the specificity of 83 ultraviolet absorption spectra from mammary gland patient pathology pieces of 83 cases. The authors chose 44 principal component data as training samples and the rest 39 as testing samples. After training discrete and continual sensation network, the authors found that the recognition rate of cancer was only 43.3% and the recognition of noncancerous one was 38.7% when using the discrete sensation network. However, because fuzzy-mathematics was introduced to the continual sensation network and the output value of this model was expanded to [0,1], the recognition rate of cancer reached 83.6% and that of noncancerous one was 76.3% when using this model.
余发军1,赵元黎1,刘伟1,吕晶2. 主成分分析结合感知器在医学光谱分类中的应用[J]. 光谱学与光谱分析, 2008, 28(10): 2396-2400.
YU Fa-jun1,ZHAO Yuan-li1,LIU Wei1,Lü Jing2. Principal Components Analysis with Sensation Network Applied in the Recognition of Medicine Spectrum. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(10): 2396-2400.
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