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Table 3 Performance of the proposed CNNs with different parameters

From: Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset

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

  1. 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
  2. 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