Copyright
©The Author(s) 2024.
World J Gastroenterol. Jan 14, 2024; 30(2): 170-183
Published online Jan 14, 2024. doi: 10.3748/wjg.v30.i2.170
Published online Jan 14, 2024. doi: 10.3748/wjg.v30.i2.170
Table 3 Effect of stage 1 multimodal module ablation experiments on the performance metrics of the algorithm
Method | Color channel module | Accuracy, % | Sensitivity, % | Specificity, % | |||
R | G | B | RGB | ||||
Method 1 | √ | × | × | × | 98.32 | 98.29 | 98.36 |
Method 2 | × | √ | × | × | 96.97 | 96.99 | 96.93 |
Method 3 | × | × | √ | × | 99.04 | 99.02 | 99.08 |
Method 4 | × | × | × | √ | 99.08 | 99.05 | 99.12 |
- Citation: Zhang RY, Qiang PP, Cai LJ, Li T, Qin Y, Zhang Y, Zhao YQ, Wang JP. Automatic detection of small bowel lesions with different bleeding risks based on deep learning models. World J Gastroenterol 2024; 30(2): 170-183
- URL: https://www.wjgnet.com/1007-9327/full/v30/i2/170.htm
- DOI: https://dx.doi.org/10.3748/wjg.v30.i2.170