Table 2.

The proposed model results are compared with the state-of-the-art network.

Method	Reference	Evaluating indexes
		Sensitivity	Specificity	PPV	NPV	Accuracy
Alexnet	Alexnet model [18]	0.86	0.92	0.91	0.94	0.91
	Maghdid et al. [19]	0.92	0.96	0.95	0.97	0.95
	Turkoglu et al. [20]	0.92	0.97	0.97	0.91	0.94
	Loey et al. [21]	0.92	0.96	0.93	0.93	0.93
Vgg	Vgg16 model [22]	0.92	0.94	0.91	0.97	0.93
	Vgg19 model [22]	0.96	0.96	0.95	0.97	0.96
	Sitaula et et al. [23]	0.96	0.96	0.94	0.97	0.96
	Shibly et al. [24]	0.97	0.95	0.93	0.98	0.96
	Lee et al. [25]	0.87	0.89	0.89	0.87	0.89
Resnet	Resnet18 model [26]	0.98	0.94	0.93	0.98	0.96
	Resnet50 model [26]	0.97	0.98	0.98	0.98	0.97
	Resnet101 model [26]	0.97	0.94	0.98	0.99	0.97
	Zhou et al. [28]	0.88	0.97	0.936	0.93	0.93
	Sakib et al. [29]	0.89	0.91	0.78	0.98	0.90
	Hira et al. [30]	0.96	0.94	0.97	0.87	0.90
Densenet	Densenet121 model [31]	0.97	0.97	0.97	0.97	0.97
	Densenet201 model [31]	0.98	0.91	0.95	0.98	0.97
	Tabrizchi et al. [32]	0.87	0.95	0.97	0.91	0.95
	Mangal et al. [33]	0.98	0.87	0.74	0.99	0.92
	Chowdhury et al. [34]	0.99	0.97	0.97	0.98	0.98
Proposed		0.99	0.98	0.99	0.99	0.99