Name
Title | ||
|---|---|---|
Label Propagation Algorithm For The Slices Detection Of A Ground-Glass Opacity Nodule |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.4018/IJSI.2019010106 | INTERNATIONAL JOURNAL OF SOFTWARE INNOVATION |
Keywords | DocType | Volume |
Computed Tomography (CT) Volume Data, Detection of the Slices Including the Ground-Glass Opacity Nodule, Ground-Glass Opacity Nodules (GGO), Label Propagation Algorithm, Lung Nodules, Semi-Supervised Learning | Journal | 7 |
Issue | ISSN | Citations |
1 | 2166-7160 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Du | 1 | 3 | 0.75 |
| Dandan Yuan | 2 | 0 | 0.34 |
| Jianming Wang | 3 | 4 | 5.82 |
| Xiaojie Duan | 4 | 0 | 0.68 |
| Yanhe Ma | 5 | 0 | 0.34 |
| Hong Zhang | 6 | 276 | 26.98 |