TABLE 5.

Performance results obtained with 70/30 training/testing ratio using different pre‐trained models on the second dataset

	Evaluation metric
Models	ACC	SEN	SPE	Preci	Mr	Fpr
AlexNet	0.9859	0.9812	0.9906	0.9905	0.0141	0.0094
DenseNet201	0.9914	1.0000	0.9828	0.9831	0.0086	0.0172
Googlenet	0.9765	0.9765	0.9765	0.9765	0.0235	0.0235
InceptionresNetv2	0.9906	0.9984	0.9828	0.9830	0.0094	0.0172
Inceptionv3	0.9922	0.9890	0.9953	0.9953	0.0078	0.0047
ResNet18	0.99	0.9984	0.9436	0.9465	0.0290	0.0564
ResNet50	0.9912	0.9859	1.0000	1.0000	0.0071	0
ResNet101	0.9929	0.9884	0.9937	0.9938	0.0039	0.0063
SqueezeNet	0.9765	0.9890	0.9639	0.9639	0.0235	0.0361
VGG16	0.9444	0.9028	0.9859	0.9846	0.0556	0.0141
VGG19	0.9647	0.9514	0.9781	0.9775	0.0353	0.0219