Table 2.

The comparison between the proposed hybrid CNN-transformer DL model and other benchmark DL models for HER2-positive and HER2-negative classification in the internal test set.

Method	AUC	Accuracy	Sensitivity	Specificity	F1 score
ViT	0.82 (0.69, 0.92)	0.79 (0.67, 0.90)	0.89 (0.73, 1.00)	0.71 (0.52, 0.89)	0.80 (0.65, 0.92)
DenseNet	0.83 (0.68, 0.95)	0.77 (0.64, 0.90)	0.78 (0.57, 0.95)	0.76 (0.58, 0.93)	0.76 (0.58, 0.89)
ResNet	0.85 (0.72, 0.96)	0.85 (0.72, 0.92)	0.83 (0.64, 1.00)	0.86 (0.70, 1.00)	0.83 (0.67, 0.95)
ConvNeXt	0.89 (0.76, 0.98)	0.85 (0.72, 0.95)	0.89 (0.73, 1.00)	0.81 (0.63, 0.95)	0.84 (0.69, 0.95)
Our Model	0.93 (0.82, 0.99)	0.90 (0.80, 0.97)	0.83 (0.65, 1.00)	0.95 (0.84, 1.00)	0.88 (0.75, 0.97)

The best results are shown in bold. The values in parentheses are confidence intervals. AUC, area under the receiver operating characteristic curve.