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

Table 6 Comparison of different efficiency metrics of different models across architectural paradigms, mean ± SD

Model	Parameters (M)	FLOPs (G)	GPU usage (GB)	Mean training time (minute)		Mean inference time (ms)		FPS
				Self-collected	EDD 2020	CPU	GPU
CNN-based
U-Net	31.46	36.95	3.30	82.79 ± 0.25	10.35 ± 0.03	215.35 ± 37.50	3.64 ± 6.321	274.731
ResNet + U-Net	32.52	8.23	2.00	57.03 ± 0.351	6.69 ± 0.121	62.67 ± 6.32	5.18 ± 0.35	193.05
ConvNeXt + UPerNet	41.37	16.71	2.50	77.63 ± 0.16	8.84 ± 0.10	102.97 ± 8.35	5.65 ± 0.40	176.99
M2SNet	29.89	13.50	2.50	89.83 ± 1.75	9.15 ± 0.07	101.97 ± 6.52	14.86 ± 1.21	67.29
Dilated SegNet	18.111	20.72	3.20	98.38 ± 0.87	10.71 ± 0.04	146.60 ± 9.76	9.23 ± 1.24	108.34
PraNet	32.56	5.30	1.801	84.06 ± 0.22	8.18 ± 0.10	65.78 ± 7.19	12.16 ± 0.78	82.24
Transformer-based
SwinV2 + UPerNet	41.91	17.19	2.70	89.07 ± 0.98	8.96 ± 0.09	126.12 ± 13.56	12.19 ± 0.65	82.03
SegFormer	24.73	4.23	2.00	71.93 ± 0.11	8.04 ± 0.19	61.48 ± 5.74	9.68 ± 0.75	103.31
SETR-MLA	90.77	18.60	3.00	71.40 ± 1.13	9.18 ± 0.17	109.13 ± 4.38	5.55 ± 0.52	180.18
TransUNet	105.00	29.33	4.50	107.16 ± 0.84	12.87 ± 0.21	204.71 ± 25.55	13.03 ± 0.71	76.75
PVTV2 + EMCAD	26.77	4.43	2.50	85.03 ± 1.18	9.08 ± 0.12	78.35 ± 9.39	12.56 ± 1.99	79.62
FCBFormer	33.09	29.98	8.10	163.76 ± 0.56	18.44 ± 0.13	305.01 ± 34.36	21.43 ± 3.49	46.66
Mamba-based
Swin-UMamba	59.89	31.46	6.00	162.35 ± 0.57	20.68 ± 0.49	NA	13.00 ± 0.68	76.92
Swin-UMamba-D	27.50	6.10	5.30	148.57 ± 0.68	17.46 ± 0.13	NA	12.97 ± 1.53	77.10
UMamba-Bot	28.77	18.68	2.90	91.31 ± 0.15	11.03 ± 0.06	NA	6.27 ± 0.54	159.49
UMamba-Enc	27.56	19.05	3.10	97.68 ± 0.33	12.07 ± 0.04	NA	7.28 ± 0.45	137.36
VM-UNETV2	22.77	4.071	3.20	108.95 ± 0.23	12.12 ± 0.21	NA	12.90 ± 7.27	77.52

¹Indicates the best value for each column.

FLOPs: Floating point operations; GPU: Graphics processing unit; CPU: Central processing unit; FPS: Frames per second; CNN: Convolutional neural network; SETR: Segmentation transformer; MLA: Multi-level feature aggregation; PVTV2: Pyramid vision transformer v2; EMCAD: Efficient multi-scale convolutional decoding; NA: Not applicable.