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

Table 8 Statistical comparison of different architecture types across the two datasets

Architecture type	Models (n)	Mean IoU (%) (95%CI)	Pairwise comparisons
Self-collected dataset
CNN-based	6	86.44 (84.11, 88.77)	vs T: T = -0.43, P = 0.68, d = 0.25
			vs M: T = 0.50, P = 0.63, d = -0.30
Transformer-based	6	87.01 (84.48, 89.55)	vs C: T = 0.43, P = 0.68, d = 0.25
			vs M: T = 0.78, P = 0.46, d = -0.46
Mamba-based	5	85.48 (80.46, 90.50)	vs C: T = -0.50, P = 0.63, d = -0.30
			vs T: T = -0.78, P = 0.46, d = -0.46
EDD2020 dataset
CNN-based	6	71.35 (65.16, 77.53)	vs T: T = -0.84, P = 0.42, d = 0.49
			vs M: T = 0.48, P = 0.64, d = -0.29
Transformer-based	6	73.97 (68.85, 79.08)	vs C: T = 0.84, P = 0.42, d = 0.49
			vs M: T = 1.23, P = 0.25, d = -0.73
Mamba-based	5	69.43 (60.34, 78.52)	vs C: T = -0.48, P = 0.64, d = -0.29
			vs T: T = -1.23, P = 0.25, d = -0.73

IoU: Intersection over union; CI: Confidence interval; CNN: Convolutional neural network; C: Convolutional neural network-based; T: Transformer-based; M: Mamba-based.