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Label Propagation Algorithm for the Slices Detection of a Ground-Glass Opacity Nodule
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Author(s): Weiwei Du (Information and Human Science, Kyoto Institute of Technology, Kyoto, Japan), Dandan Yuan (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China), Jianming Wang (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China), Xiaojie Duan (School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China), Yanhe Ma (Tianjin Chest Hospital, Tianjin, China)and Hong Zhang (Tianjin Chest Hospital, Tianjin, China)
Copyright: 2019
Volume: 7
Issue: 1
Pages: 15
Source title:
International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.2019010106
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Abstract
A radiologist must read hundreds of slices to recognize a malignant or benign lung tumor in computed tomography (CT) volume data. To reduce the burden of the radiologist, some proposals have been applied with the ground-glass opacity (GGO) nodules. However, the GGO nodules need be detected and labeled by a radiologist manually. Some slices with the GGO nodule can be missed because there are many slices in several volume data. Although some papers have proposed a semi-supervised learning method to find the slices with GGO nodules, the was no discussion on the impact of parameters in the proposed semi-supervised learning. This article also explains and analyzes the label propagation algorithm which is one of the semi-supervised learning methods to detect the slices including the GGO nodules based on the parameters. Experimental results show that the proposal can detect the slices including the GGO nodules effectively.
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