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

Table 5 Performance metrics of different segmentation models on the self-collected dataset and the EDD2020 dataset, mean ± SD

Model	Encoder size	Self-collected dataset			EDD2020 dataset
		Pixel accuracy (%)	IoU (%)	Dice score (%)	Pixel accuracy (%)	IoU (%)	Dice score (%)
CNN-based
U-Net	NA	89.22 ± 0.16	82.14 ± 0.21	88.47 ± 0.17	93.41 ± 0.07	67.63 ± 0.48	79.37 ± 0.44
ResNet +U-Net	ResNet501	92.20 ± 0.14	87.30 ± 0.30	91.95 ± 0.19	94.52 ± 0.31	73.97 ± 1.29	83.59 ± 1.05
ConvNeXt + UPerNet	ConvNeXt-T1	93.05 ± 0.14	88.48 ± 0.09	92.76 ± 0.10	95.17 ± 0.13	76.90 ± 0.61	85.65 ± 0.48
M2SNet	Res2Net50-v1b-26w-4s1	92.17 ± 0.17	86.93 ± 0.32	91.72 ± 0.26	94.80 ± 0.22	74.81 ± 1.01	84.24 ± 0.79
Dilated SegNet	ResNet501	92.43 ± 0.35	87.47 ± 0.51	92.04 ± 0.32	94.46 ± 0.24	73.64 ± 0.88	83.35 ± 0.78
PraNet	Res2Net50-v1b-26w-4s1	92.38 ± 0.29	86.35 ± 0.43	91.31 ± 0.31	94.48 ± 0.07	61.12 ± 0.41	74.15 ± 0.34
Transformer-based
SwinV2 + UPerNet	SwinV2-T1	93.15 ± 0.11	88.50 ± 0.18	92.84 ± 0.12	95.18 ± 0.23	76.97 ± 0.89	85.59 ± 0.65
Segformer	MiT-B21	93.39 ± 0.232	88.94 ± 0.38	93.14 ± 0.27	95.25 ± 0.31	77.20 ± 0.98	85.90 ± 0.76
SETR-MLA	ViT-B-161	90.09 ± 0.43	83.37 ± 0.24	89.19 ± 0.37	94.17 ± 0.36	71.48 ± 1.43	82.14 ± 1.23
TransUNet	ResNet501 and ViT-B-161	90.67 ± 0.35	84.55 ± 0.55	90.02 ± 0.40	92.74 ± 0.18	65.06 ± 1.33	77.35 ± 1.16
EMCAD	PVTV2-B21	93.33 ± 0.19	88.74 ± 0.22	93.01 ± 0.16	95.22 ± 0.22	77.07 ± 0.91	85.81 ± 0.67
FCBFormer	PVTV2-B21	93.04 ± 0.21	87.96 ± 0.34	92.48 ± 0.28	95.04 ± 0.15	76.03 ± 0.70	85.18 ± 0.45
Mamba-based
Swin-UMamba	VSSM-encoder1	92.57 ± 0.10	87.78 ± 0.10	92.20 ± 0.06	93.81 ± 0.30	71.12 ± 1.26	81.23 ± 0.86
Swin-UMamba-D	VSSM-D-encoder1	93.39 ± 0.19	89.06 ± 0.202	93.19 ± 0.142	95.37 ± 0.092	77.53 ± 0.322	86.15 ± 0.222
UMamba-Bot	NA	88.47 ± 0.20	81.43 ± 0.14	87.38 ± 0.22	92.04 ± 0.14	61.79 ± 0.86	75.03 ± 0.80
UMamba-Enc	NA	87.72 ± 0.11	80.74 ± 0.13	86.64 ± 0.16	92.12 ± 0.29	61.82 ± 0.56	75.24 ± 0.60
VM-UNETV2	VM-UNET-encoder1	93.09 ± 0.29	88.38 ± 0.34	92.76 ± 0.33	94.89 ± 0.14	74.89 ± 0.47	84.36 ± 0.42

¹Indicates pre-trained with ImageNet-1K dataset.

²Indicates the highest value for each column.

IoU: Intersection over union; CNN: Convolutional neural network; SETR: Segmentation transformer; MLA: Multi-level feature aggregation; PVTV2: Pyramid vision transformer v2; VSSM: Visual-state-space-model; EMCAD: Efficient multi-scale convolutional decoding; NA: Not applicable.