Copyright
©The Author(s) 2023.
World J Gastroenterol. Feb 7, 2023; 29(5): 879-889
Published online Feb 7, 2023. doi: 10.3748/wjg.v29.i5.879
Published online Feb 7, 2023. doi: 10.3748/wjg.v29.i5.879
Table 2 Comparison of model accuracy
Network type | PPV (%) | NPV (%) | mIOU | PA (%) | Parameter (M) | Float calculation amount (G) | Time (s) |
PSPNet | 85.14 | 98.62 | 0.64 | 98 | 51.43 | 829.10 | 0.9 |
DeeplabV3+ | 45.07 | 99.75 | 0.59 | 89 | 59.34 | 397.00 | 0.95 |
UperNet | 92.55 | 95.69 | 0.69 | 98 | 126.08 | 34.94 | 0.9 |
Our model | 94.27 | 98.74 | 0.69 | 99 | 46.38 | 467.2 | 0.6 |
- Citation: Chu Y, Huang F, Gao M, Zou DW, Zhong J, Wu W, Wang Q, Shen XN, Gong TT, Li YY, Wang LF. Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy. World J Gastroenterol 2023; 29(5): 879-889
- URL: https://www.wjgnet.com/1007-9327/full/v29/i5/879.htm
- DOI: https://dx.doi.org/10.3748/wjg.v29.i5.879