Name
Abstract | ||
|---|---|---|
Accurate tongue image segmentation is helpful to acquire correct automatic tongue diagnosis result. However, traditional methods cannot bring satisfying results in most cases. This paper proposes an end-to-end trainable tongue image segmentation method using deep convolutional neural network based on ResNet. The proposed method, named DeepTongue, segments tongue by using a forward network without preprocessing. The proposed method has no restrictions of the illumination and size of tongue images. Experimental results show that the proposed DeepTongue improves the segmentation accuracy by a noticeable margin. In addition, DeepTongue is much faster than the existing tongue image segmentation methods. |
Year | Venue | Field |
|---|---|---|
2018 | ICASSP | Pattern recognition,Convolutional neural network,Segmentation,Computer science,Image segmentation,Preprocessor,Artificial intelligence,Residual neural network,Tongue |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingqian Lin | 1 | 0 | 1.01 |
| Junwei Xle | 2 | 0 | 0.34 |
| Cui-Hua Li | 3 | 74 | 13.24 |
| Yanyun Qu | 4 | 216 | 38.66 |