Table 4.
Neural network architecture
| layer number | structure of each layer | kernel size | layer input | layer output |
|---|---|---|---|---|
| 1 | Conv3D,ReLU | 2,(8, 8, 8) | 4,(16,16,16) | 2,(16,16,16) |
| 2 | Conv3D,ReLU | 4,(8, 8, 8) | 2,(16,16,16) | 4,(16,16,16) |
| 3 | MaxPooling3D | (2,2,2) | 4,(16,16,16) | 4,(8,8,8) |
| 4 | Dropout(0.25) | NA | 4,(8,8,8) | 4,(8,8,8) |
| 5 | Conv3D,ReLU | 8,(4, 4, 4) | 4,(8,8,8) | 8,(8,8,8) |
| 6 | Conv3D,ReLU | 16,(4, 4, 4) | 8,(8,8,8) | 16,(8,8,8) |
| 7 | MaxPooling3D | (2,2,2) | 16,(8,8,8) | 16,(4,4,4) |
| 8 | Conv3D,ReLU | 32,(2, 2, 2) | 16,(4,4,4) | 32,(4,4,4) |
| 9 | Conv3D,ReLU | 64,(2, 2, 2) | 32,(4,4,4) | 64,(4,4,4) |
| 10 | Dropout(0.25) | NA | 32,(4,4,4) | 64,(4,4,4) |
| 11 | Flatten | NA | 32,(4,4,4) | 64,(4,4,4) |
| 12 | Dense(128),ReLU | NA | 4096 | 128 |
| 13 | Dropout(0.5) | NA | 128 | 128 |
| 14 | Dense(1),sigmoid | NA | 128 | 1 |