Table 8.

Performance evaluation metrics for all proposed AI methods

		C1	C2	C3	C4	C5	C6	C7	C8	C9
		Sp	Se	Precision	Recall	Acc (%)	F1-Score	DOR	AUC*	Kappa
R1	VGG16	0.630	0.802	0.69	0.80	71.87	0.741	6.93	0.714	0.434
R2	DenseNet169	0.810	0.895	0.86	0.89	85.93	0.877	36.85	0.852	0.711
R3	ANN	0.815	0.914	0.86	0.914	87.058	0.88	47.60	0.861	0.736
R4	DenseNet201	0.835	0.907	0.88	0.90	87.49	0.893	49.70	0.871	0.747
R5	MobileNet	0.864	0.937	0.9	0.93	90.93	0.918	96.00	0.893	0.807
R6	DenseNet121	0.888	0.938	0.92	0.93	91.56	0.929	122.67	0.913	0.831
R7	DT	0.943	0.969	0.96	0.969	95.882	0.964	536	0.948	0.915
R8	RF	0.985	0.99	0.99	0.99	99.41	0.99	6831	0.988	0.976
R9	CNN	0.985	0.99	0.99	0.99	99.41	0.99	6831	0.991	0.976

*all p values <0.0001