TEE standard plane classification based on improved multi-class AdaBoost algorithm
WANG Lili1,2, FU Zhongliang1,2, TAO Pan1,2, ZHU Kai1,2
1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu Sichuan 610041, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:Due to redundancy of ultrasound image samples, high similarity between different planes caused by disease, and inaccurate positioning of region-of-interest, a classification method of TransEsophageal Echocardiography (TEE) standard plane was proposed by combining with Bag of Features (BOF) model, active learning and improved multi-class AdaBoost algorithm. Firstly, BOF model was constructed to describe ultrasound image. Secondly, active learning was adopted to select the most informative samples for classifiers as training data set. Lastly, improved multi-class AdaBoost algorithm was proposed, where the weight update rule of multi-class AdaBoost was modified according to the classfication results of temporary strong learner, and the TEE standard plane was classified by the improved multi-class AdaBoost algorithm. The experimental results on TEE data set and three UCI data sets showed that, compared with AdaBoost.SAMME, multi-class Support Vector Machine (SVM), BP neural network and AdaBoost.M2, the G-mean value, the total classification accuracy and the classification accuracy in most classes of the proposed method were improved in varying degrees, the classification accuracy of easily misclassified class was improved most significantly. The experimental results illustrate that the improved multi-class AdaBoost algorithm can significantly improve the G-mean value and accuracy of easily misclassified class in the datasets containing similar samples between classes.
王莉莉, 付忠良, 陶攀, 朱锴. 基于多分类AdaBoost改进算法的TEE标准切面分类[J]. 计算机应用, 2017, 37(8): 2253-2257.
WANG Lili, FU Zhongliang, TAO Pan, ZHU Kai. TEE standard plane classification based on improved multi-class AdaBoost algorithm. Journal of Computer Applications, 2017, 37(8): 2253-2257.
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