Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases.
A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized N-dimensional Principal Component Analysis (GND-PCA) is used to establish the statistical fractal model...
| Publicado en: | BioMed Research International Vol. 2013; pp. 656391 - 656392 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104113723&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104113723 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104113723 104113723 2012371332 NLM24224174 PMC3809934 104113723 ppf: 656391 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Statistical Fractal Models Based on GND-PCA and Its Application on Classification of Liver Diseases. aug: au: Jiang, Huiyan Feng, Tianjiao Zhao, Di Yang, Benqiang Zhang, Libo Chen, Yenwei affil: Software College, Northeastern University, Shenyang 110819, China. sug: subj: Mathematics Liver Diseases Classification Models, Statistical Algorithms Human Image Processing, Computer Assisted Liver Diseases Epidemiology Factor Analysis ab: A new method is proposed to establish the statistical fractal model for liver diseases classification. Firstly, the fractal theory is used to construct the high-order tensor, and then Generalized N-dimensional Principal Component Analysis (GND-PCA) is used to establish the statistical fractal model and select the feature from the region of liver; at the same time different features have different weights, and finally, Support Vector Machine Optimized Ant Colony (ACO-SVM) algorithm is used to establish the classifier for the recognition of liver disease. In order to verify the effectiveness of the proposed method, PCA eigenface method and normal SVM method are chosen as the contrast methods. The experimental results show that the proposed method can reconstruct liver volume better and improve the classification accuracy of liver diseases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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