A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays

被引:27
作者
Livieris, Ioannis E. [1 ]
Kanavos, Andreas [1 ]
Tampakas, Vassilis [1 ]
Pintelas, Panagiotis [2 ]
机构
[1] Technol Educ Inst Western Greece, Dept Comp & Informat Engn, DISK Lab, GR-26334 Antirion, Greece
[2] Univ Patras, Dept Math, GR-26500 Patras, Greece
关键词
machine learning; semi-supervised learning; self-labeled algorithms; classifiers; ensemble learning; weighted voting; image classification; lung abnormalities; TUBERCULOSIS;
D O I
10.3390/a12030064
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the explicit classification information of labeled data with the information hidden in the unlabeled data. In the present work, we propose a new ensemble semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays based on a new weighted voting scheme. The proposed algorithm assigns a vector of weights on each component classifier of the ensemble based on its accuracy on each class. Our numerical experiments illustrate the efficiency of the proposed ensemble methodology against other state-of-the-art classification methods.
引用
收藏
页数:15
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