Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel Low-cost U-Net (LCU-Net) for the Environmental Microorganism (EM) image segmentation task to assist microbiologists in detecting and identifying EMs more effectively. The LCU-Net is an improved Convolutional Neural Network (CNN) based on U-Net, Inception, and concatenate operations. It addresses the limitation of single receptive field setting and the relatively high memory cost of U-Net. Experimental results show the effectiveness and potential of the proposed LCU-Net in the practical EM image segmentation field. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.107885 | Pattern Recognition |
Keywords | DocType | Volume |
Environmental miroorganisms,Image segmentation,Deep convolutional neural networks,Low-cost | Journal | 115 |
Issue | ISSN | Citations |
1 | 0031-3203 | 1 |
PageRank | References | Authors |
0.35 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinghua Zhang | 1 | 1 | 0.35 |
| Chen Li | 2 | 4 | 5.18 |
| Sergey Kosov | 3 | 4 | 1.11 |
| Marcin Grzegorzek | 4 | 185 | 48.00 |
| Kimiaki Shirahama | 5 | 108 | 22.43 |
| Tao Jiang | 6 | 211 | 44.26 |
| Changhao Sun | 7 | 1 | 1.37 |
| Zihan Li | 8 | 1 | 1.03 |
| Hong Li | 9 | 42 | 16.40 |