Table 4.

Comparison of the diagnostic performance of EDLC-TN with other four state-of-the-art algorithms.

	AUC	Sensitivity (%)	Specificity (%)	Accuracy (%)
EDLC-TN	0.941 (0.936–0.946)	93.77	94.44	98.51
ResNeXt	0.882 (0.875–0.889)*	85.53	90.86	82.83
SE_Inception_v4	0.874 (0.866–0.881)*	90.33	84.38	97.12
SE_Net	0.840 (0.832–0.848)*	88.64	79.35	96.52
Xception	0.880 (0.872–0.887)*	84.68	91.26	93.84

EDLC-TN – ensemble deep learning classification model of thyroid nodules; AUC – area under the ROC curve; AUCs of EDLC-TN and other three models were calculated by the method of DeLong et al. P – The difference of AUCs between the EDLC-TN and other four models was compared by Z-test,

^*P<0.05.