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Table 11 Experiments result of CapsNet

From: Deep convolutional neural network based medical image classification for disease diagnosis

Sr. no

Configuration

CapsNet

1

Aug0

0.748

2

Aug1

0.788

3

Aug1 with 64 feature maps and 64 input size

0.737

4

Aug1 with 64 feature maps and 128 input size

0.627

5

Aug1 with 32 feature maps and 64 input size

0.798

6

Aug1 with 32 feature maps and 128 input size

0.784

7

Aug1 with 32 feature maps and 48 input size

0.756

8

Aug1 with 24 feature maps and 64 input size

0.798

9

Aug1 with 16 feature maps and 64 input size

0.765

10

Aug1 with 32 feature maps, 64 input size and half primary capsule (4)

0.811

11

Aug1 with 32 feature maps, 64 input size, half primary capsule (4) and half capsule channel (16)

0.825

12

Aug1 with 32 feature maps, 64 input size, half primary capsule (4) and one fourth capsule channel (8)

0.752

13

Aug1 with 24 feature maps, 64 input size, half primary capsule (4) and half capsule channel (16)

0.825

14

Aug1 with 32 feature maps, 64 input size, half primary capsule (4) and half capsule channel (16) with more image by augmentation (10,000)

0.788