TABLE 1.

Performance of binary classification on the DFU infection dataset.

Network	Accuracy	Sensitivity	Precision	Specificity	F-measure	AUC score
ResNet-18	74.20 ± 1.25	76.66 ± 2.82	73.08 ± 4.13	71.98 ± 2.98	74.72 ± 2.08	82.23 ± 1.26
ResNet-50	73.79 ± 1.31	76.96 ± 3.13	72.33 ± 2.93	70.75 ± 0.92	74.50 ± 1.98	81.44 ± 1.60
ResNet-101	74.63 ± 0.98	75.97 ± 0.88	73.88 ± 2.65	73.26 ± 1.37	74.89 ± 1.63	82.52 ± 0.81
ResNet-152	74.82 ± 1.02	76.80 ± 0.67	73.82 ± 2.42	72.82 ± 1.95	75.25 ± 1.17	82.78 ± 0.85
RegNetY-4GF	73.63 ± 1.50	75.92 ± 1.97	72.57 ± 2.72	71.37 ± 2.30	74.16 ± 1.53	81.33 ± 1.54
RegNetY-8GF	74.85 ± 1.51	76.72 ± 2.18	73.91 ± 3.18	73.00 ± 2.66	75.24 ± 1.89	81.90 ± 1.60
RegNetY-16GF	75.41 ± 0.95	77.42 ± 1.48	74.44 ± 2.61	73.51 ± 1.82	75.85 ± 0.75	83.02 ± 1.28
EfficientNet-B0	73.95 ± 1.06	77.81 ± 2.48	72.23 ± 3.51	70.19 ± 3.00	74.83 ± 1.75	81.77 ± 1.01
EfficientNet-B2	73.85 ± 1.21	77.64 ± 1.15	72.13 ± 3.34	70.09 ± 2.88	74.73 ± 1.70	81.58 ± 0.90
EfficientNet-B4	73.61 ± 1.17	77.24 ± 2.28	72.05 ± 3.78	70.16 ± 3.07	74.46 ± 1.66	81.70 ± 1.08
EfficientNet-B6	73.43 ± 0.54	75.77 ± 1.85	72.40 ± 3.30	71.29 ± 1.69	73.97 ± 1.14	80.98 ± 1.10
EfficientNet-B7	72.79 ± 1.53	72.14 ± 3.10	73.10 ± 2.60	73.43 ± 3.16	72.54 ± 1.67	80.08 ± 1.26
MoCo	74.97 ± 2.01	74.06 ± 1.75	75.47 ± 4.37	75.96 ± 4.08	74.68 ± 2.22	82.77 ± 1.46
DeiT-S	73.65 ± 0.64	77.22 ± 1.79	72.02 ± 3.18	70.14 ± 1.89	74.47 ± 1.62	80.90 ± 0.95
DeiT-B	73.97 ± 0.83	78.09 ± 2.23	72.12 ± 3.40	69.97 ± 2.42	74.91 ± 1.63	81.58 ± 1.35
DeiT-S-D	73.98 ± 1.97	78.06 ± 2.07	72.21 ± 4.28	70.09 ± 3.81	74.93 ± 2.23	81.51 ± 1.81
DeiT-B-D	75.82 ± 1.96	79.96 ± 2.88	73.86 ± 3.33	71.88 ± 2.14	76.72 ± 2.11	83.26 ± 2.36
CKB-DeiT-S-D	75.18 ± 1.27	76.91 ± 2.15	74.36 ± 3.79	73.54 ± 3.44	75.53 ± 1.78	82.66 ± 1.28
CKB-DeiT-B-D	78.00 ± 0.93	79.16 ± 1.74	77.38 ± 2.68	77.00 ± 1.51	78.20 ± 0.94	84.78 ± 1.30