Table 3.

Comparative analysis of the classification performance of the trained TwinCNN using the Softmax, KNN, RF, MLP and DTree algorithms.

Classifier	Histology		Mammography
	Accuracy	AUC	Accuracy	AUC
KNN	0.788806	0.83349	0.780933	0.607248
RF	0.938817	0.917418	0.799797	0.673917
MLP	0.952187	0.932702	0.791684	0.632857
DTree	0.341491	0.763873	0.791684	0.637675
Softmax	0.708698	–	0.794726	–
Avg	0.755325	0.861871	0.791024	0.637924