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

Table 9 Performance comparison of different models evaluated on the self-collected dataset and through cross-validation on the test split of the EDD2020 dataset, mean ± SD

Model	Self-collected dataset			Cross validation on EDD2020 dataset			GRR (%)
	Pixel accuracy (%)	IoU (%)	Dice score (%)	Pixel accuracy (%)	IoU (%)	Dice score (%)
CNN-based
U-Net	89.22 ± 0.16	82.14 ± 0.21	88.47 ± 0.17	67.77 ± 1.17	53.72 ± 1.28	67.77 ± 1.17	65.41
ResNet + U-net	92.20 ± 0.14	87.30 ± 0.30	91.95 ± 0.19	71.62 ± 1.09	58.52 ± 1.49	71.62 ± 1.09	67.03
ConvNeXt + UPerNet	93.05 ± 0.14	88.48 ± 0.09	92.76 ± 0.10	73.60 ± 0.42	60.87 ± 0.72	73.60 ± 0.42	68.79
M2SNet	92.17 ± 0.17	86.93 ± 0.32	91.72 ± 0.26	71.98 ± 0.24	58.80 ± 0.23	71.98 ± 0.24	67.64
Dilated SegNet	92.43 ± 0.35	87.47 ± 0.51	92.04 ± 0.32	72.26 ± 0.38	59.54 ± 0.52	72.26 ± 0.38	68.08
PraNet	92.38 ± 0.29	86.35 ± 0.43	91.31 ± 0.31	70.17 ± 1.45	56.81 ± 1.82	70.17 ± 1.45	65.79
Transformer-based
SwinV2 + UPerNet	93.15 ± 0.11	88.50 ± 0.18	92.84 ± 0.12	72.96 ± 0.71	60.29 ± 0.62	72.96 ± 0.71	68.12
SegFormer	93.39 ± 0.231	88.94 ± 0.38	93.14 ± 0.27	74.36 ± 1.12	62.36 ± 1.06	74.36 ± 1.12	70.11
SETR-MLA	90.09 ± 0.43	83.37 ± 0.24	89.19 ± 0.37	71.78 ± 2.48	58.08 ± 2.76	71.78 ± 2.48	69.67
TransUNet	90.67 ± 0.35	84.55 ± 0.55	90.02 ± 0.40	70.25 ± 1.24	56.77 ± 1.43	70.25 ± 1.24	67.14
PVTV2 + EMCAD	93.33 ± 0.19	88.74 ± 0.22	93.01 ± 0.16	75.24 ± 1.31	63.35 ± 1.441	75.24 ± 1.31	71.38
FCBFormer	93.04 ± 0.21	87.96 ± 0.34	92.48 ± 0.28	75.32 ± 0.551	62.91 ± 0.63	75.32 ± 0.551	71.521
Mamba-based
Swin-UMamba	92.57 ± 0.10	87.78 ± 0.10	92.20 ± 0.06	74.43 ± 1.23	62.26 ± 1.72	74.43 ± 1.23	70.93
Swin-UMamba-D	93.39 ± 0.19	89.06 ± 0.201	93.19 ± 0.141	73.83 ± 1.68	61.77 ± 1.48	73.83 ± 1.68	69.36
UMamba-Bot	88.47 ± 0.20	81.43 ± 0.14	87.38 ± 0.22	67.03 ± 2.52	52.76 ± 2.86	67.03 ± 2.52	64.78
UMamba-Enc	87.72 ± 0.11	80.74 ± 0.13	86.64 ± 0.16	66.35 ± 1.96	52.74 ± 2.31	66.35 ± 1.96	65.33
VM-UNETV2	93.09 ± 0.29	88.38 ± 0.34	92.76 ± 0.33	73.82 ± 1.73	61.41 ± 2.09	73.82 ± 1.73	69.49

¹Indicates the highest value for each column.

IoU: Intersection over union; GRR: Generalization retention rate; CNN: Convolutional neural network; SETR: Segmentation transformer; MLA: Multi-level feature aggregation; PVTV2: Pyramid vision transformer v2; EMCAD: Efficient multi-scale convolutional decoding.