Name
Title | ||
|---|---|---|
Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network. |
Abstract | ||
|---|---|---|
•A novel weighted path convolutional neural network (CNN) architecture, called WP-CNN, is proposed to classify the diabetic retinopathy and achieves an accuracy of 94.23% with sensitivity of 90.94%, specificity of 95.74%, an area under the receiver operating curve of 0.9823 and F1-score of 0.9087.•The WP-CNN can be built by stacking weighted path blocks. The output of the weighted block can obtain the accurate diagnosis feature and reducing the multipath feature redundancy.•Comparing with the state-of-art CNN architectures, WP-CNN can be trained faster and obtain the better classification performance with only one third of convolution layers number. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.artmed.2019.07.002 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Diabetic retinopathy,Eye fundus images,Deep learning,Convolutional neural network | Multipath propagation,Receiver operating characteristic,Pattern recognition,Convolutional neural network,Computer science,Redundancy (engineering),Rate of convergence,Artificial intelligence,Deep learning,Backpropagation,Ensemble learning,Machine learning | Journal |
Volume | ISSN | Citations |
99 | 0933-3657 | 1 |
PageRank | References | Authors |
0.41 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Peng Liu | 1 | 1 | 1.76 |
| Zhanqing Li | 2 | 1 | 0.41 |
| Cong Xu | 3 | 1 | 0.41 |
| Jing Li | 4 | 36 | 12.28 |
| Ronghua Liang | 5 | 376 | 42.60 |