Table 3. The proposed model with different structures.

	DNN-8	DNN-9	DNN-10	DNN-11	DNN-12	DNN-13	DNN-14	DNN-15	DNN-16	DNN-17
# of convolutional layer	2	3	4	2	4	5	5	4	2	2
Convolutional layer dim	2D	2D	2D	2D	2D	2D	2D	2D	2D	2D
# of filters	64, 64	128, 64, 64	128, 128, 64, 64	64, 64	64, 64, 32, 32	256, 128, 64, 32, 16	64, 64, 32, 32, 16	128, 64, 32, 16	32, 32	64, 64
Kernel size	(3,3), (3,3)	(3,3), (3,3), (3,3)	(3,3), (3,3), (3,3), (3,3)	(3,3), (3,3)	(3,3), (3,3), (3,3), (3,3)	(3,3), (3,3), (3,3), (3,3), (3,3)	(3,3), (3,3), (3,3), (3,3), (3,3)	(3,3), (3,3), (3,3), (3,3)	(3,3), (3,3)	(3,3), (3,3)
# of Pooling layer	1	1	1	1	1	1	1	1	1	Without pooling
Size of max-pooling	(2, 2)	(2, 2)	(2, 2)	(2, 2)	(2, 2)	(2, 2)	(2, 2)	(2, 2)	(2, 2)	–
# of dense layers	2	2	2	2	2	2	2	2	2	2
Activation function	ReLU + sigmoid (last dense layer)	ReLU + sigmoid (last dense layer)	ReLU + sigmoid (last dense layer)	Tanh + sigmoid (last dense layer)	ReLU + sigmoid (last dense layer)	ReLU + sigmoid (last dense layer)	Tanh + (ReLU & sigmoid: dense layers)	ReLU + sigmoid (last dense layer)	ReLU + sigmoid (last dense layer)	ReLU + sigmoid (last dense layer)
Loss function	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy	Binary cross entropy
Optimizer, Learning rate	Adam,	Adam,	Adam,	Adam,	Adam,	Adam,	Adam,	SGD,	Adam,	Adam,
	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01
# of epochs	100	100	100	100	100	100	100	100	100	100
Batch size	1,024	1,024	1,024	1,024	1,024	1,024	1,024	1,024	1,024	1,024
Dropout rate	Without dropout	Without dropout	Without dropout	Without dropout	Without dropout	0.2 (before dense layers)	Without dropout	Without dropout	0.2 (between dense layesrs)	0.2 (between dense layesrs)