[The study of SVM-based recognition of particles in urine sediment].
This article used support vector machine (SVM) algorithm to recognize the particles in urine sediment in this paper. After feature extraction, cross-validation method and the contour chart of the accuracy were implemented to select the kernel function and the parameters of SVM, and according to the characteristics of SVM classifier and sample data, Multi-SVMs with two-level-classifier was successfully designed and A classification matrix was eventually obtained. The evaluation by using clinical data and comparative results with the artificial neural network have demonstrated that the proposed algorithm gets better results.
Fu
Cong
C
Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027.
Xia
Shun-Ren
SR
Zhang
Zan-Chao
ZC
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D010316:Particle Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D014556:Urine / Q000737:chemistry*