Table 3.

Performance of the proposed CNNs with different parameters

Kernel size	Kernel number	Channels of normalization	Output of FC	Dropout probability	Pooling type	Batch size	Epochs	Learning rate	Validation Favg	Elapsed time
5	6	3	120	0.5	Max	128	50	0.01	0.9758	846.53
10	6	3	120	0.5	Max	128	50	0.01	0.9688	1077.55
5	3	3	120	0.5	Max	128	50	0.01	0.9609	806.078
5	6	1	120	0.5	Max	128	50	0.01	0.9657	997.13
5	6	3	240	0.5	Max	128	50	0.01	0.9765	1282.90
5	6	3	120	0.2	Max	128	50	0.01	0.9688	1593.91
5	6	3	120	0.1	Max	128	50	0.01	0.9541	1589.37
5	6	3	120	0.5	Avg	128	50	0.01	0.9682	1606.79
5	6	3	120	0.5	Max	256	50	0.01	0.9659	905.27
5	6	3	120	0.5	Max	128	80	0.01	0.9722	2660.79
5	6	3	120	0.5	Max	128	50	0.00001	0.9855	1566.26
5	6	3	120	0.5	Max	128	50	0.0001	0.9917	1609.34

The kernel size and the kernel number are only related to the convolutional layer. FC represents the first fully connected layer in our proposed CNNs. The elapsed time indicates the time for training the CNN in different epochs

The Italic in the first row indicates the default setting of parameters. The Italic in final row indicates the final optimized setting of parameters. The Italic in other rows indicates the modifed parameter compared with the default setting