Table 5.

Performance parameters obtained using Resnet50-TCN and proposed model.

Performance Measurements	Resnet50-TCN
	MLP					SVM
	1stFold	2ndFold	3rdFold	4thFold	5thFold	1stFold	2ndFold	3rdFold	4thFold	5thFold
Accuracy	99.700	99.401	99.401	98.902	98.902	99.401	98.503	98.503	99.201	99.201
True Positive	1000	996	996	991	991	997	987	987	994	994
False Positive	3	6	6	11	11	6	15	15	8	8
Kappa	0.9955	0.991	0.991	0.9835	0.9835	0.991	0.997	0.977	0.988	0.988
TP Rate	0.997	0.994	0.994	0.989	0.989	0.994	0.985	0.985	0.992	0.992
FP Rate	0.001	0.003	0.003	0.005	0.005	0.003	0.007	0.007	0.004	0.004
Precision	0.997	0.994	0.994	0.989	0.989	0.994	0.985	0.985	0.992	0.992
Recall	0.997	0.994	0.994	0.989	0.989	0.994	0.985	0.985	0.992	0.992
F1-measure	0.997	0.994	0.994	0.989	0.989	0.994	0.985	0.985	0.992	0.992
MCC	0.996	0.991	0.991	0.984	0.984	0.991	0.978	0.978	0.988	0.988
ROC Area	1.000	0.999	0.999	1.000	1.000	0.997	0.992	0.992	0.996	0.996
PRC Area	1.000	0.999	0.999	1.000	1.000	0.992	0.977	0.977	0.989	0.989
Performance Measurement	RESCOVIDTCNNet
	MLP					SVM
	1stFold	2ndFold	3rdFold	4thFold	5thFold	1stFold	2ndFold	3rdFold	4thFold	5thFold
Accuracy	99.700	99.501	99.501	98.902	99.900	99.700	99.201	99.201	98.902	99.800
True Positive	1000	997	997	991	1001	1000	994	994	991	1002
False Positive	3	5	5	11	1	3	8	8	11	0
Kappa	0.9955	0.9925	0.9925	0.9835	0.9983	0.995	0.988	0.988	0.9835	0.997
TP Rate	0.997	0.995	0.995	0.989	0.999	0.997	0.992	0.992	0.989	0.998
FP Rate	0.001	0.002	0.002	0.005	0.000	0.001	0.004	0.004	0.005	0.001
Precision	0.997	0.995	0.995	0.989	0.999	0.997	0.992	0.992	0.989	0.998
Recall	0.997	0.995	0.995	0.989	0.999	0.997	0.992	0.992	0.989	0.998
F1-measure	0.997	0.995	0.995	0.989	0.999	0.997	0.992	0.992	0.989	0.998
MCC	0.996	0.993	0.993	0.984	0.999	0.996	0.988	0.988	0.984	0.998
ROC Area	1.000	0.999	0.999	1.000	0.999	0.999	0.996	0.996	0.993	1.000
PRC Area	1.000	0.999	0.999	0.999	0.998	0.997	0.989	0.989	0.983	1.000