TABLE 2.

CNN Architecture, Activations, and Parameters

Layer	Name	Activations	Parameters
1	Tensor input layer	[725,725,3]
2	2D convolution layer	[239,239,64]	Weights [11,11,3,64], bias [1,1,64]
3	Batch normalization	[239,239,64]	Offset and scale [1,1,64]
4	ReLU layer	[239,239,64]
5	Max pooling layer	[119,119,64]	Size [3,3], stride [2,2], padding [0,0,0,0]
6	2D convolution layer	[40,40,128]	Weights 5,5,64,128], bias [1,1,128]
7	Batch normalization	[40,40,128]	Offset and scale [1,1,128]
8	ReLU layer	[40,40,128]
9	Max pooling layer	[19,19,128]	Size [3,3], stride [2,2], padding [0,0,0,0]
10	2D convolution layer	[19,19,256]	Weights [3,3,128,256], bias [1,1,256]
11	Batch normalization	[19,19,256]	Offset and scale [1,1,256]
12	ReLU layer	[19,19,256]
13	Max pooling layer	[9,9,256]	Size [3,3], stride [2,2], padding [0,0,0,0]
14	2D convolution layer	[9,9,192]	Weights [3,3,256,192], Bias [1,1,192]
15	Batch normalization	[9,9,192]	Offset and scale [1,1,192]
16	ReLU layer	[9,9,192]
17	Max pooling layer	[4,4,192]	Size [3,3], stride [2,2], padding [0,0,0,0]
18	2D convolution layer	[4,4,192]	Weights [3,3,256,192], bias [1,1,192]
19	Batch normalization	[4,4,192]	Offset and scale [1,1,192]
20	ReLU layer	[4,4,192]
21	Max pooling layer	[1,1,192]	Size [3,3], stride [2,2], padding [0,0,0,0]
22	Fully connected layer	[1,1,192]	Weights [192,192], bias [192,1]
23	ReLU layer	[1,1,192]
24	Dropout layer	[1,1,192]	0.5
25	Fully connected layer	[1,1,86]	Weights [86,192], bias [86,1]
26	ReLU layer	[1,1,86]
27	Dropout layer	[1,1,86]	0.5
28	Fully connected layer	[1,1,2]	Weights [2,86], bias [2,1]
29	Softmax layer	[1,1,2]
30	Classification layer		Cross entropy loss function