| | Network layer type | Size | Output dimension |
| BaseNet | Input layer | - | (1, 1, 64, 64) | Convolutional Layer | 32 5 × 5 Convolution kernel | (1, 32, 64, 64) | Max pooling layer | 2 × 2, stride 1 | (1, 32, 32, 32) | Dropout layer | - | (1, 32, 32, 32) | Convolutional layer | 64 5 × 5 Convolution kernel | (1, 64, 32, 32) | Max pooling layer | 2 × 2, stride 1 | (1, 64, 16, 16) | Dropout layer | - | (1, 64, 16, 16) | Fully connected layer | Logistic regression | (1024, 1) | Dropout layer | - | (1024, 1) | Output layer | - | 1 |
| VGGNet | Input layer | - | (1, 1, 64, 64) | Convolutional layer | 32 3 × 3 Convolution kernel | (1, 32, 64, 64) | Convolutional layer | 16 3 × 3 Convolution kernel | (1, 16, 64, 64) | Max pooling layer | 2 × 2, stride 1 | (1, 16, 32, 32) | Dropout layer | - | (1, 16, 32, 32) | Convolutional layer | 32 3 × 3 Convolution kernel | (1, 32, 32, 32) | Max pooling layer | 2 × 2, stride 1 | (1, 32, 16, 16) | Dropout layer | - | (1, 32, 16, 16) | Fully connected layer | 512 maxout unit | (32, 512) | Dropout layer | - | (32, 512) | Fully connected layer | Logistic regression | (32, 1) | Dropout layer | - | (32, 1) | Output layer | - | 1 |
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