Abstract
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.
Highlights
The abnormal destruction of this red blood cell can cause many diseases to occur [1,2], the diagnosis of hemolysis in clinic is mostly obtained by the observation of the medical staff on hemolysis and condensation reaction [34], this method is simple and easy, but it is easy to observe the small amount of hemolysis is susceptible to subjective factors, only to adapt to the obvious samples of hemolysis, and in order to be able to accurately and quickly make correct judgments, medical staff need to carry out a long period of learning to accumulate experience, work efficiency is relatively low
Machine learning based on SVM can be used as classification and regression analysis based on structural risk minimization [7], so the SVM classification algorithm is used to classify the hemolytic samples, but because the SVM algorithm has a smooth effect on the data, the prediction of the samples with mutation characteristics is not good, We introduced the adaboost algorithm [8] to enhance the SVM classifier: The Learning weights of SVM sample learning are adjusted, the repetition training obtains different SVM classifier, until the preset classifier number is reached, and all SVM classifier is weighted linearly, a strong classifier is obtained
The proposed Adaboost classification algorithm based on SVM is not affected by the subjective factors, and the hemolysis judgment which is similar to the erythrocyte counting method is provided and effectively
Summary
The abnormal destruction of this red blood cell can cause many diseases to occur [1,2], the diagnosis of hemolysis in clinic is mostly obtained by the observation of the medical staff on hemolysis and condensation reaction [34], this method is simple and easy, but it is easy to observe the small amount of hemolysis is susceptible to subjective factors, only to adapt to the obvious samples of hemolysis, and in order to be able to accurately and quickly make correct judgments, medical staff need to carry out a long period of learning to accumulate experience, work efficiency is relatively low. Machine learning[5,6] is the understanding of human learning and thinking mode by cognitive science and physiology, and establishes the cognitive model or computational model of cognition process based on human thought. Machine learning based on SVM can be used as classification and regression analysis based on structural risk minimization [7], so the SVM classification algorithm is used to classify the hemolytic samples, but because the SVM algorithm has a smooth effect on the data, the prediction of the samples with mutation characteristics is not good, We introduced the adaboost algorithm [8] to enhance the SVM classifier: The Learning weights of SVM sample learning are adjusted, the repetition training obtains different SVM classifier, until the preset classifier number is reached, and all SVM classifier is weighted linearly, a strong classifier is obtained. Through comparing the classification algorithm with other methods, it shows that the classification of blood samples based on SVM algorithm can make effective and accurate hemolysis judgment quickly and effectively
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