Urine Sediment Recognition Method Based on SVM and AdaBoost

It is an important method to help doctor's clinical diagnosis that using pattern recognition technology recognizes and counts Urine Sediment's visible component. Harr wavelet feature has good property of distinguish different components, the proposed method using AdaBoost to select a little part typical Harr feature which are taken as input data of SVM. The trained several bi-class SVM classifiers corresponding with different components are composed into a multi-class classifier. In order to improve algorithm's speed, cascade accelerating algorithm is used. It is shown by experiment that the proposed method not only can effectively recognize different visible component of Urine sediment but also improve precision.