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Table 3 Performance of 3D model alternatives designed with different values for depth in convolution kernels.

From: Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

 

L(1): Conv(3x3x1) followed by L(r):Conv(3x3x3)

L(a): Conv(3x3x3)

L(1): Conv(3x3x1) followed by L(r): Conv(3x3x6)

L(a): Conv(3x3x6)

CV

T acc.

V acc.

T acc.

V acc.

T acc.

V acc.

T acc.

V acc.

2-F

0.76 ±0.04

0.58±0.12

0.77 ±0.03

0.59 ±0.14

0.69 ±0.04

0.42 ±0.12

0.7 ±0.03

0.54 ±0.14

3-F

0.83 ±0.04

0.7 ±0.02

0.83 ±0.05

0.71 ±0.03

0.73 ±0.02

0.68 ±0.02

0.73 ±0.02

0.66 ±0.02

4-F

0.79 ±0.02

0.7 ±0.02

0.8 ±0.02

0.7 ±0.02

0.76 ±0.04

0.66 ±0.02

0.75 ±0.04

0.66 ±0.03

5-F

0.8 ±0.04

0.71 ±0.02

0.82 ±0.03

0.72 ±0.02

0.78 ±0.04

0.7 ±0.02

0.75 ±0.03

0.68 ±0.02

6-F

0.82 ±0.03

0.72 ±0.02

0.83 ±0.04

0.72 ±0.02

0.77 ±0.03

0.68 ±0.02

0.76 ±0.03

0.68 ±0.02

7-F

0.81 ±0.03

0.72 ±0.02

0.87 ±0.03

0.72 ±0.02

0.77 ±0.04

0.7 ±0.02

0.77 ±0.03

0.69 ±0.02

8-F

0.84 ±0.03

0.71 ±0.02

0.86 ±0.03

0.72 ±0.02

0.81 ±0.04

0.7 ±0.02

0.76 ±0.03

0.70 ±0.02

9-F

0.86 ±0.03

0.72 ±0.02

0.87 ±0.03

0.72 ±0.02

0.82 ±0.04

0.69 ±0.02

0.82 ±0.04

0.71 ±0.02

10-F

0.86 ±0.03

0.72 ±0.02

0.87 ±0.03

0.72 ±0.02

0.83 ±0.03

0.70 ±0.02

0.82 ±0.03

0.71 ±0.01

  1. L(1) represents the first convolution layer, L(r) represents the remaining convolution layers, L(a) represents all of the convolution layers in the developed 3D model. The values show the obtained accuracy followed by 95% confidence interval, while T and V represent training and validation accuracy, respectively. Bold represents the best value achieved