Colorectal Cancer Diagnostic Algorithm Based on Sub-Patch Weight Color Histogram in Combination of Improved Least Squares Support Vector Machine for Pathological Image.
In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color his...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
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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=138200115&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200115 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200115 138200115 138200115 10.1007/s10916-019-1429-8 138200115 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Colorectal Cancer Diagnostic Algorithm Based on Sub-Patch Weight Color Histogram in Combination of Improved Least Squares Support Vector Machine for Pathological Image. aug: au: Yang, Kai Zhou, Bi Yi, Fei Chen, Yan Chen, Yingsheng affil: Department of Radiological Intervention, Shanghai Sixth People's Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science, 201306, Shanghai, China sug: subj: Colorectal Neoplasms Diagnosis Algorithms Diagnosis, Computer Assisted Methods Image Processing, Computer Assisted Image Interpretation, Computer Assisted Image Enhancement Sensitivity and Specificity Human ab: In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color histogram to classify pathological image. Firstly, the pathological image is divided into non-overlapping sub-patches, and the features of sub-patch histogram are extracted. Then, the global and local features are fused by the sub-patch weighting algorithm. Then, the RelicfF based forward selection algorithm is used to integrate color features and texture features so as to enhance the characterization capabilities of the tumor cell. Finally, Morlet wavelet kernel-based least squares support vector machine method is adopted to enhance the generalization ability of the model for small sample with non-linear and high-dimensional pattern classification problems. Experimental results show that the proposed pathological diagnostic algorithm can gain higher accuracy compared with existing comparison algorithms. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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