Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study

Table 7 Performance and efficiency comparison of different models across architectural paradigms, mean ± SD

Model	Parameters (M)	FLOPs (G)	Mean inference time on GPU (ms)	IoU (%)		Average IoU (%)	PET score (%)
				Self-collected	EDD 2020
CNN-based
U-Net	31.46	36.95	3.64 ± 6.321	82.14 ± 0.21	67.63 ± 0.48	74.88	45.74
ResNet + U-Net	32.52	8.23	5.18 ± 0.35	87.30 ± 0.30	73.97 ± 1.29	80.63	82.58
ConvNeXt + UPerNet	41.37	16.71	5.65 ± 0.40	88.48 ± 0.09	76.90 ± 0.61	82.69	84.70
M2SNet	29.89	13.50	14.86 ± 1.21	86.93 ± 0.32	74.81 ± 1.01	80.87	72.33
Dilated SegNet	18.111	20.72	9.23 ± 1.24	87.47 ± 0.51	73.64 ± 0.88	80.55	74.88
PraNet	32.56	5.30	12.16 ± 0.78	86.35 ± 0.43	61.12 ± 0.41	73.74	48.81
Transformer-based
SwinV2 + UPerNet	41.91	17.19	12.19 ± 0.65	88.50 ± 0.18	76.97 ± 0.89	82.74	78.41
SegFormer	24.73	4.23	9.68 ± 0.75	88.94 ± 0.38	77.20 ± 0.98	82.86	92.021
SETR-MLA	90.77	18.60	5.55 ± 0.52	83.37 ± 0.24	71.48 ± 1.43	77.42	52.45
TransUNet	105.00	29.33	13.03 ± 0.71	84.55 ± 0.55	65.06 ± 1.33	74.81	26.39
PVTV2 + EMCAD	26.77	4.43	12.56 ± 1.99	88.74 ± 0.22	77.07 ± 0.91	82.91	88.14
FCBFormer	33.09	29.98	21.43 ± 3.49	87.96 ± 0.34	76.03 ± 0.70	82.00	61.89
Mamba-based
Swin-UMamba	59.89	31.46	13.00 ± 0.68	87.78 ± 0.10	71.12 ± 1.26	79.45	53.31
Swin-UMamba-D	27.50	6.10	12.97 ± 1.53	89.06 ± 0.201	77.53 ± 0.321	83.291	88.39
UMamba-Bot	28.77	18.68	6.27 ± 0.54	81.43 ± 0.14	61.79 ± 0.86	71.61	39.45
UMamba-Enc	27.56	19.05	7.28 ± 0.45	80.74 ± 0.13	61.82 ± 0.56	71.28	37.15
VM-UNETV2	22.77	4.071	12.90 ± 7.27	88.38 ± 0.34	74.89 ± 0.47	81.63	83.48

¹Indicates the best value for each column.

FLOPs: Floating point operations; GPU: Graphics processing unit; IoU: Intersection over union; PET: Performance-efficiency trade-off; CNN: Convolutional neural network; SETR: Segmentation transformer; MLA: Multi-level feature aggregation; PVTV2: Pyramid vision transformer v2; EMCAD: Efficient multi-scale convolutional decoding.