Table 1.

Comparison architecture of the proposed feature extractor with AlexNet CNN.

Layer index	AlexNet CNN		The proposed feature extractor
	Layer name	Learnable parameters	Layer name	Learnable parameters
1	Input: 227 × 227 × 3	0	Input: 227 × 227 × 3	0
2	Convolution	34,944	Convolution	34,944
[3]	ReLU	0	LeakyReLU	0
4	Normalization	0	Normalization	0
5	Pooling	0	Pooling	0
6	Convolution	307,456	Convolution	307,456
[7]	ReLU	0	LeakyReLU	0
8	Normalization	0	Normalization	0
9	Pooling	0	Pooling	0
10	Convolution	885,120	Convolution	885,120
[11]	ReLU	0	LeakyReLU	0
12	Convolution	663,936	Convolution	663,936
[13]	ReLU	0	LeakyReLU	0
14	Convolution	442,624	Convolution	442,624
[15]	ReLU	0	LeakyReLU	0
16	Pooling	0	Pooling	0
17	Fully connected	37,752,832	Fully connected	37,752,832
[18]	ReLU	0	LeakyReLU	0
19	Dropout	0	Dropout	0
[20]	Fully connected: 1 × 4096	16,781,312	Fully connected: 1 × 512	2,097,664
[21]	–	–	LeakyReLU	0
[22]	–	–	Dropout	0
[23]	–	–	Fully connected: 1 × 64	32,832