Name
Title | ||
|---|---|---|
An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network. |
Abstract | ||
|---|---|---|
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JBHI.2017.2725903 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Lungs,Computed tomography,Feature extraction,Cancer,Image segmentation,Databases,Biomedical imaging | Computer vision,Pattern recognition,Medical imaging,Segmentation,Computer science,Image processing,Image segmentation,Feature extraction,Artificial intelligence,Deep learning,Artificial neural network,False positive paradox | Journal |
Volume | Issue | ISSN |
22 | 4 | 2168-2194 |
Citations | PageRank | References |
12 | 0.53 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyang Jiang | 1 | 25 | 3.51 |
| He Ma | 2 | 12 | 2.56 |
| W. Qian | 3 | 155 | 22.21 |
| Mengdi Gao | 4 | 21 | 3.11 |
| Yan Li | 5 | 399 | 95.68 |