Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation

Table 1 Comparative model summary

Model name	Architecture family	Pretraining	Mean IoU, %	Mean Dice	PET score, %	GRR, %	Inference latency (milliseconds/frame)	Model size	Notes
U-Net	CNN	N/A	74.88	(Self 79.37 + EDD2020 67.63)/2 = 73.50%	45.74	65.41	3.64	31.46 M, approximately 126 MB	Classic encoder-decoder with skip connections; excellent throughput with very high FPS but lower PET; simple architecture may limit generalization
ResNet + U-Net	CNN	ResNet encoder typically ImageNet-1K	80.63	(83.59 + 73.97)/2 = 78.78%	82.58	67.03	5.18	32.52 M, approximately 130 MB	Residual backbone improves accuracy and robustness with modest compute; good PET
ConvNeXt + UPerNet	CNN	Typically ImageNet-1K	82.69	(85.65 + 76.90)/2 = 81.27%	84.70	68.79	5.65	41.37 M, approximately 165 MB	Modern CNN with ViT-inspired design; strong accuracy and good throughput
M2SNet	CNN	N/A	80.87	(84.24 + 74.81)/2 = 79.53%	72.33	67.64	14.86	29.89 M, approximately 120 MB	Multi-scale subtraction units for improved feature complementarity and edge clarity; slower inference among CNNs
Dilated SegNet	CNN	ResNet50 backbone	80.55	(83.35 + 73.64)/2 = 78.49%	74.88	68.08	9.23	18.111 M, approximately 72 MB	Dilated convolutions for real-time polyp segmentation; good trade-off, moderate speed
PraNet	CNN	N/A	73.74	(74.15 + 61.12)/2 = 67.63%	48.81	65.79	12.16	32.56 M, approximately 130 MB	Parallel partial decoder + reverse attention for boundary refinement; decent accuracy but lower PET and generalization
SwinV2 + UPerNet	Transformer	SwinV2 typically ImageNet-pretrained	82.74	(85.59 + 76.97)/2 = 81.28%	78.41	68.12	12.19	41.91 M, approximately 168 MB	SwinV2 hierarchical transformer backbone with UPerNet decoder; strong accuracy with moderate compute
SegFormer	Transformer	Typically ImageNet-1K	82.86	(93.14 + 77.20)/2 = 85.17%	92.02	70.11	9.68	24.73 M, approximately 99 MB	Excellent performance-efficiency balance; low FLOPs (4.23 GFLOPs), good generalization: Recommended for real-time clinical use per paper
SETR-MLA	Transformer	ViT backbone	77.42	(82.14 + 71.48)/2 = 76.81%	52.45	69.67	5.55	90.77 M, approximately 363 MB	Segmentation transformer with multi-level aggregation; large parameter count but relatively fast inference in this setup
TransUNet	Hybrid (CNN + transformer)	ResNet50 + ViT	74.81	(77.35 + 65.06)/2 = 71.21%	26.39	67.14	13.03	105.00 M, approximately 420 MB	Combines CNN encoder and ViT: Strong representational power but heavy compute and lower PET
PVTV2 + EMCAD	Transformer	PVTV2 usually ImageNet-pretrained	82.91	(85.81 + 77.07)/2 = 81.44%	88.14	71.38	12.56	26.77 M, approximately 107 MB	Pyramid vision transformer v2 + efficient multi-scale decoding; strong generalization and good PET
FCBFormer	Transformer	PVTV2 backbone	82.00	(85.18 + 76.03)/2 = 80.61%	61.89	71.52	21.43	33.09 M, approximately 132 MB	Polyp-specialized transformer variant; strong generalization (highest GRR) but highest inference time among many models, limiting real-time use at high resolution
Swin-UMamba	Hybrid (Mamba based)	N/A	79.45	(81.23 + 71.12)/2 = 76.18%	53.31	70.93	13.00	59.89 M, approximately 240 MB	Mamba hybrid leveraging visual-state-space-model; good generalization but relatively large and slower training/inference cost
Swin-UMamba-D	Hybrid (Mamba based)	N/A	83.29	(86.15 + 77.53)/2 = 81.84%	88.39	69.36	12.97	27.50 M, approximately 110 MB	Best segmentation performance (average IoU) among study but relatively high training and inference cost; strong segmentation accuracy but moderate generalization
UMamba-Bot	Hybrid (Mamba based)	N/A	71.61	(75.03 + 61.79)/2 = 68.41%	39.45	64.78	6.27	28.77 M, approximately 115 MB	Lightweight mamba variant; good FPS but weaker accuracy and generalization
UMamba-Enc	Hybrid (Mamba based)	N/A	71.28	(75.24 + 61.82)/2 = 68.53%	37.15	65.33	7.28	27.56 M, approximately 110 MB	Encoder-focused mamba variant; similar trade-offs to UMamba-Bot: Faster but lower accuracy
VM-UNETV2	Hybrid (Mamba like)	N/A	81.63	(84.36 + 74.89)/2 = 79.63%	83.48	69.49	12.90	22.77 M, approximately 91 MB	VM-UNET encoder variant; strong PET and competitive accuracy, GPU-focused design

IoU: Intersection over union; PET: Performance-efficiency trade-off; GRR: Generalization-reliability rate; CNN: Convolutional neural network; N/A: Not applicable; FPS: Frames per second.