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

Table 2 Comparison between the single and ensemble models on CVC_Colon (clear) datasets

Model	SegNet		UNet		Attention-UNet		ResNet-UNet		HarDMSEG
	IoU	Dice	IoU	Dice	IoU	Dice	IoU	Dice	IoU	Dice
Snap_max	0.700	0.780	0.713	0.788	0.754	0.830	0.747	0.819	0.840	0.901
Ens_max	0.702	0.779	0.711	0.783	0.752	0.829	0.750	0.816	0.840	0.901
Improve	0.002	-0.001	-0.002	-0.005	-0.002	-0.002	0.003	-0.002	0.000	0.000
Improve (%)	0.30	-0.08	-0.28	-0.61	-0.22	-0.22	0.42	-0.26	0.03	-0.01

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.