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

Table 3 Comparison between the single and ensemble models on CVC_Clinic (clear) datasets

Model	SegNet		UNet		Attention-UNet		ResNet-UNet		HarDMSEG
	IoU	Dice	IoU	Dice	IoU	Dice	IoU	Dice	IoU	Dice
Snap_max	0.814	0.890	0.816	0.884	0.836	0.900	0.823	0.884	0.845	0.893
Ens_max	0.815	0.890	0.826	0.891	0.838	0.898	0.824	0.884	0.844	0.892
Improve	0.001	0.001	0.010	0.007	0.002	-0.001	0.001	0.000	-0.001	-0.001
Improve (%)	0.13	0.07	1.20	0.76	0.23	-0.14	0.08	0.02	-0.08	-0.06

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. IoU: Intersection over union.