Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization

Table 4

Performances of different architectures.

Model	Cross validation
	AC (%)	SE (%)	SP (%)
ResNet-18(480)	91.00 ± 0.70	90.55 ± 0.86	91.45 ± 1.84
hyper(l2) FC-only	91.64 ± 0.79	91.22 ± 1.08	92.05 ± 1.65
hyper(l3) FC-only	91.62 ± 0.65	91.15 ± 0.56	92.07 ± 1.45
hyper(l23) FC-only	91.66 ± 0.81	91.48 ± 0.90	91.83 ± 1.75
hyper(l2) all-update	91.39 ± 1.02	91.28 ± 1.04	91.47 ± 1.86
hyper(l3) all-update	91.50 ± 0.81	90.63 ± 1.06	92.33 ± 1.74
hyper(l23) all-update	91.37 ± 0.72	91.33 ± 0.63	91.4 ± 1.42
hyper(l2) ImageNet	90.96 ± 0.90	90.52 ± 1.10	91.38 ± 2.01
hyper(l3) ImageNet	91.04 ± 0.80	90.52 ± 1.34	91.54 ± 1.31
hyper(l23) ImageNet	90.82 ± 0.85	90.26 ± 1.33	91.37 ± 1.48