[Retracted] A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning

Table 1

Results of the informative sample selection method and the all samples selection approach under different pretrained CNNs.

CNN type	Annotation method	Precision (%)	Recall (%)	F1 score (%)	Accuracy (%)	AUC (%)
AlexNet	All samples	94.35 ± 2.66	89.12% ± 2.16	91.62 ± 1.01	92.85 ± 0.91	97.75% ± 0.59
	Informative samples	93.05 ± 2.71	90.18% ± 2.46	91.54 ± 1.29	92.69 ± 1.16	97.88 ± 0.26
VGG16	All samples	91.49 ± 1.49	90.18 ± 1.12	90.81 ± 0.51	92.00 ± 0.49	98.15 ± 0.36
	Informative samples	92.03 ± 1.13	91.05 ± 0.38	91.54 ± 0.60	92.62 ± 0.56	97.25 ± 0.26
ResNet50	All samples	95.29 ± 2.64	89.47 ± 5.73	92.16 ± 2.57	93.38 ± 2.04	98.86 ± 0.56
	Informative samples	94.09 ± 3.34	95.09 ± 2.24	94.52 ± 0.87	95.15 ± 0.87	98.93 ± 0.31
Se_ResNet50	All samples	96.06 ± 1.54	93.51 ± 0.98	94.76 ± 0.75	95.46 ± 0.67	99.06 ± 0.16
	Informative samples	96.99 ± 0.75	90.18 ± 2.46	93.44 ± 1.36	95.06 ± 1.09	98.94 ± 0.28