Table 2 Describing in detail the architecture of the 3D CNN
From: Using 3D CNN for classification of Parkinson’s disease from resting-state fMRI data
Layer | Feature map | Stride | Kernel | Dropout rate | Activation | Pool size | Regularization (l2 = 0.05) |
|---|---|---|---|---|---|---|---|
Convolution | 128 | \(1\times 1\times 1\) | 3 \(\times 3\times 3\) | - | ReLU | - | L2 |
Max pooling | - | - | - | - | - | \(2\times 2\times 2\) | - |
Convolution | 256 | \(1 \times 1\times 1\) | \(2 \times 2 \times 2\) | - | ReLU | - | L2 |
Max pooling | - | - | - | - | - | \(2\times 2\times 2\) | - |
Convolution | 512 | \(1 \times 1 \times 1\) | \(2 \times 2 \times 2\) | - | ReLU | - | L2 |
Dropout | - | - | - | 0.2 | - | - | - |
Max pooling | - | - | - | - | - | \(2\times 2\times 2\) | - |
Batch normalization (momentum = 0.6) | - | - | - | - | - | - | - |
Flatten | - | - | - | - | - | - | - |
Dense | 256 | - | - | - | ReLU | - | L2 |
Dense | 4096 | - | - | - | ReLU | - | L2 |
Dropout | - | - | - | 0.3 | - | - | - |
Classification | - | - | - | - | Sigmoid | - | L2 |