Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework

Table 1

Training analysis (%) of the proposed IoT-enabled deep ensemble model for automated diagnosis of COVID-19 suspected cases on the four-class chest CT dataset when training to testing ratio is 65 : 35.

Model	Accuracy	F-measure	AUC	Recall	Precision
JLM [16]	97.88	98.27	98.07	98.97	97.83
WSDL [17]	98.11	98.56	98.33	99.14	97.89
IPCNN [18]	97.93	97.63	97.78	98.54	97.38
DeCNN [19]	98.45	97.53	97.99	98.89	97.82
DLCRD [20]	98.43	97.64	98.03	98.94	97.73
PARL [22]	98.67	97.44	98.05	98.76	98.12
AGGDF [24]	98.17	97.65	97.91	98.72	97.65
GCNN [25]	98.68	97.58	98.13	98.97	98.21
GoogLeNet [53]	98.16	98.35	98.25	99.08	97.62
ResNet152V2 [44]	98.55	98.33	98.44	99.27	97.85
DenseNet201 [34]	98.57	98.18	98.34	99.09	97.83
IRNV2 [3]	98.18	97.48	97.83	98.67	97.56
Proposed	99.12	98.91	98.79	99.28	99.08