Table 5.

Performance evaluation of the proposed FDEIoL using various DL models on the CXR images dataset.

Classifiers	Evaluation Parameters	Classes				Overall Accuracy	Error Rate	Cohen’s Kappa	Average F1Score
		Covid-19	PNA	TB	Normal
HincV3XGBoost	Precision	0.99	0.98	1.00	1.00	0.9888	0.0112	0.990001	0.9925
	Recall	0.99	0.99	1.00	0.99
	F1-Score	0.99	0.99	1.00	0.99
	Specificity	0.99	0.99	1.00	1.00
LT-ViT	Precision	0.90	0.92	0.96	0.98	0.9429	0.0571	0.9171	0.9375
	Recall	0.95	0.89	0.97	0.94
	F1-Score	0.92	0.91	0.96	0.96
	Specificity	0.97	0.96	0.98	0.99
BM-Net	Precision	0.99	0.98	1.00	0.99		0.0085	0.9932	0.99
	Recall	0.99	0.98	1.00	0.99	0.9915
	F1-Score	0.99	0.98	1.00	0.99
	Specificity	0.99	1.00	1.00	1.00
VGG-SCNets	Precision	1.00	0.97	0.97	1.00		0.0208	0.9700	0.985
	Recall	1.00	0.97	0.98	1.00	0.9792
	F1-Score	1.00	0.97	0.97	1.00
	Specificity	0.99	0.99	0.99	0.98
MEEDNets	Precision	0.98	1.00	1.00	1.00	0.9919	0.0081	0.9932	0.9925
	Recall	0.99	0.99	1.00	0.99
	F1-Score	0.99	0.99	1.00	0.99
	Specificity	0.99	1.00	1.00	1.00
ResGANet	Precision	0.96	0.97	1.00	0.96		0.021	0.9666	0.975
	Recall	0.96	0.96	1.00	0.98	0.9790
	F1-Score	0.96	0.97	1.00	0.97
	Specificity	0.98	0.99	1.00	0.98
Ensemble Except FL Modeling	Precision	0.98	0.99	0.99	0.98	0.9795	0.0205	0.980002	0.96
	Recall	0.98	0.96	1.00	1.00
	F1-Score	0.98	0.98	0.89	0.99
	Specificity	0.99	0.99	0.99	0.99
Proposed FDEIoL	Precision	1.00	0.99	1.00	1.00	0.9924	0.0076	0.996667	0.995
	Recall	1.00	1.00	1.00	0.99
	F1-Score	1.00	0.99	1.00	0.99
	Specificity	1.00	1.00	1.00	1.00