Table 1.

Convolutional neural Network model summary.

Layer (Type)	Output Shape	Param #
conv2d (Conv2D)	(None, 66, 66, 18)	504
max_pooling2d (MaxPooling2D)	(None, 33, 33, 18)	0
dropout (Dropout)	(None, 33, 33, 18)	0
conv2d_1 (Conv2D)	(None, 31, 31, 72)	11,736
max_pooling2d_1 (MaxPooling2D)	(None, 15, 15, 72)	0
dropout_1 (Dropout)	(None, 15, 15, 72)	0
conv2d_2 (Conv2D)	(None, 13, 13, 72)	46,728
max_pooling2d_2 (MaxPooling2D)	(None, 6, 6, 72)	0
dropout_2 (Dropout)	(None, 6, 6, 72)	0
conv2d_3 (Conv2D)	(None, 4, 4, 72)	46,728
max_pooling2d_3 (MaxPooling2D)	(None, 2, 2, 72)	0
dropout_3 (Dropout)	(None, 2, 2, 72)	0
flatten (Flatten)	(None, 288)	0
dense (Dense)	(None, 72)	20,808
dropout_4 (Dropout)	(None, 72)	0
dense_1 (Dense)	(None, 72)	146