Name
Title | ||
|---|---|---|
Classification Of Pulmonary Emphysema In Ct Images Based On Multi-Scale Deep Convolutional Neural Networks |
Abstract | ||
|---|---|---|
In this work, we aim at classifying emphysema in computed tomography (CT) images of lungs. Most previous works are limited to extracting low-level features or mid-level features without enough high-level information. Moreover, these approaches do not take the characteristics (scales) of different emphysema into account, which are crucial for feature extraction. In contrast to previous works, we propose a novel deep learning method based on multi-scale deep convolutional neural networks. There are three contributions for this paper. First, we propose to use a base residual network with 20 layers to extract more high-level information. To the best of our knowledge, this is the first deep learning method for classification of emphysema. Second, we incorporate multi-scale information into our deep neural networks so as to take full consideration of the characteristics of different emphysema. Finally, we established a high-quality emphysema dataset which contains 91 high-resolution computed tomography (HRCT) volumes, annotated manually by two experienced radiologists and checked by one experienced chest radiologist. A 92.68% classification accuracy is achieved on this dataset. The results show that (1) the multi-scale method is highly effective in comparison to the single scale setting; (2) the proposed approach is superior to the state-of-the-art techniques. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Emphysema, Computed Tomography Image, Tissue Classification, Multi-Scale, Deep Learning |
Field | DocType | ISSN |
Computer vision,Residual,Pattern recognition,Convolutional neural network,Computer science,Feature extraction,Computed tomography,Artificial intelligence,Deep learning,Deep neural networks | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liying Peng | 1 | 2 | 2.87 |
| Lanfen Lin | 2 | 78 | 24.70 |
| Hongjie Hu | 3 | 11 | 9.50 |
| Huali Li | 4 | 2 | 1.52 |
| Xiaoli Ling | 5 | 2 | 1.18 |
| Dan Wang | 6 | 101 | 40.29 |
| Xian-Hua Han | 7 | 14 | 10.19 |
| Yutaro Iwamoto | 8 | 13 | 17.95 |
| Yen-Wei Chen | 9 | 720 | 155.73 |