Name
Abstract | ||
|---|---|---|
Classification of bone tumor plays an important role in treatment. As artificial diagnosis is in low efficiency, an automatic classification system can help doctors analyze medical images better. However, most existing methods cannot reach high classification accuracy on clinical images because of the high similarity between images. In this paper, we propose a super label guided convolutional neural network (SG-CNN) to classify CT images of bone tumor. Images with two hierarchical labels would be fed into the network, and learned by its two sub-networks, whose tasks are learning the whole image and focusing on lesion area to learn more details respectively. To further improve classification accuracy, we also propose a multi-channel enhancement (ME) strategy for image preprocessing. Owing to the lack of suitable public dataset, we introduce a CT image dataset of bone tumor. Experimental results on this dataset show our SG-CNN and ME strategy improve the classification accuracy obviously. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01421-6_13 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT II |
Keywords | Field | DocType |
Bone tumor classification, Super label guided convolutional neural network, Multi-channel enhancement | Pattern recognition,Computer science,Convolutional neural network,Preprocessor,Artificial intelligence | Conference |
Volume | ISSN | Citations |
11140 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Li | 1 | 659 | 125.00 |
| Wenyu Zhou | 2 | 0 | 0.34 |
| Guiwen Lv | 3 | 0 | 0.34 |
| Luo Guibo | 4 | 15 | 6.04 |
| Zhu Yuesheng | 5 | 112 | 39.21 |
| Ji Liu | 6 | 15 | 5.59 |