title: [Principal components analysis with sensation network applied in the recognition of medicine spectrum].
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.
authors.affiliation: School of Physical Engineering, Zhengzhou University, Zhengzhou 450001, China. yufajun1982@sohu.com
authors.forename: Fa-jun
authors.initials: FJ
authors.lastname: Yu
authors.forename: Yuan-li
authors.initials: YL
authors.lastname: Zhao
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Jing
authors.initials: J
authors.lastname: Lü
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000378:metabolism; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000378:metabolism; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis / Q000379:methods*; D013056:Spectrophotometry, Ultraviolet