Table 3.

The Comparison study of UDLVAE with other existing methods.

Methods	Sensitivity	Specificity	Accuracy	F-measure
UDLVAE (Binary Class)	0.985	0.988	0.987	0.988
UDLVAE (Multi Class)	0.994	0.993	0.992	0.992
DWS-CNN (Binary Class)	0.984	0.986	0.985	0.986
DWS-CNN (Multi Class)	0.991	0.992	0.991	0.990
FR-CNN	0.977	0.955	0.974	0.985
ResNet-50	0.930	0.677	0.896	0.939
Inception V3	0.910	0.742	0.887	0.933
AlexNet	0.925	0.714	0.905	0.946
CovxNet	0.905	0.958	0.917	0.911
CapsNet	0.842	0.918	0.892	0.842
VGG19	0.971	0.960	0.963	0.942
Deep Transfer Learning	0.896	0.920	0.908	0.904
Multi-Layer Perceptron	0.930	0.872	0.931	0.930
Logistic Regression	0.930	0.903	0.921	0.920
K-Nearest Neighbour	0.890	0.907	0.889	0.890
Decision Tree	0.870	0.889	0.867	0.870