Name
Abstract | ||
|---|---|---|
This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/EMBC.2015.7318458 | EMBC |
Field | DocType | Volume |
Learning machine,Grading (education),Computer science,Convolutional neural network,Brain tumor,Test data,Artificial intelligence,Deep learning,Artificial neural network,Machine learning | Conference | 2015 |
ISSN | Citations | PageRank |
1557-170X | 12 | 0.80 |
References | Authors | |
5 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuehao Pan | 1 | 12 | 0.80 |
| Weimin Huang | 2 | 25 | 3.46 |
| Zhiping Lin | 3 | 291 | 37.46 |
| Wanzheng Zhu | 4 | 12 | 2.49 |
| Jiayin Zhou | 5 | 128 | 17.50 |
| Jocelyn Wong | 6 | 12 | 0.80 |
| Zhongxiang Ding | 7 | 20 | 2.32 |