Table 1.

Description of the proposed CNN model

Name	#Filters	Filter size	Stride	Padding	Weighs	Output
Input layer of size [224 224 3]
Conv_1	32	$3\times 3$	$2\times 2$	Same	$3\times 3\times 3\times 32$	$112\times 112\times 32$
Batch Normalization + ReLU
Pool_1	–	$3\times 3$	$3\times 3$	[0 0 0 0]	–	$37\times 37\times 32$
Conv_2	128	$5\times 5$	$2\times 2$	Same	$5\times 5\times 32\times 128$	$19\times 19\times 128$
Batch Normalization + ReLU
Pool_2	–	$3\times 3$	$3\times 3$	[0 0 0 0]	–	$6\times 6\times 128$
Conv_3	256	$5\times 5$	$1\times 1$	Same	$5\times 5\times 128\times 256$	$3\times 3\times 256$
Batch normalization + ReLU
GAP	–	–	$6\times 6$	[0 0 0 0]	–	$1\times 1\times 256$
Two fully-connected layer					$2\times 256$
Softmax layer
Classification output layer						2