Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning

Table 1 Comparison between the single and ensemble models on GZ_Capcam (artifact-affected) datasets

Model	SegNet		U-Net		Attention-UNet		ResNet-UNet		HarDMSEG
Metric	IoU	Dice	IoU	Dice	IoU	Dice	IoU	Dice	IoU	Dice
Snap_max	0.341	0.454	0.379	0.507	0.361	0.502	0.414	0.522	0.538	0.649
Ens_max	0.341	0.462	0.406	0.532	0.375	0.509	0.435	0.537	0.550	0.653
Improve	0.000	0.008	0.027	0.025	0.014	0.007	0.021	0.015	0.012	0.004
Improve (%)	0.08	1.71	7.01	4.93	3.88	1.40	5.13	2.86	2.22	0.61

Snap_max and Ens_max denote the performance of the best performing single model and ensemble model, respectively, and the last two rows denote the improvement of the ensemble learning model compared to a single model. GZ_Capcam: The test set of experimental images from Guangzhou First People’s Hospital. IoU: Intersection over union.