Hemolysis detection based on SVM of Adaboost classification algorithm

摘要

Aiming at the problem that clinical hemolysis is difficult to be observed and judged, a method of Adaboost learning classification based on SVM is proposed. The method firstly extracts the basic features of the target area of the blood sample, such as the average of the gray level, the standard deviation of the gray level and the appearance frequency of the particles, as the input eigenvectors of the learning, and carries out SVM weak learner learning. Subsequently, Adaboost algorithm is used to measure the weak learner Set linear weighting, so as to enhance the strong learning device; Finally, online testing, calculation of test sample hemolytic degree and classification. The Adaboost learning classification test based on SVM is compared with the macroscopic and red blood cell counting methods. The experimental results show that the learning-based classification testing method achieves higher detection accuracy without subjective factors and has the highest detection efficiency.