Table 2: The list of two CNN network structures parameters.

Network layer typeSizeOutput dimension

BaseNetInput layer-(1, 1, 64, 64)
Convolutional Layer32 5 × 5 Convolution kernel(1, 32, 64, 64)
Max pooling layer2 × 2, stride 1(1, 32, 32, 32)
Dropout layer-(1, 32, 32, 32)
Convolutional layer64 5 × 5 Convolution kernel(1, 64, 32, 32)
Max pooling layer2 × 2, stride 1(1, 64, 16, 16)
Dropout layer-(1, 64, 16, 16)
Fully connected layerLogistic regression(1024, 1)
Dropout layer-(1024, 1)
Output layer-1

VGGNetInput layer-(1, 1, 64, 64)
Convolutional layer32 3 × 3 Convolution kernel(1, 32, 64, 64)
Convolutional layer16 3 × 3 Convolution kernel(1, 16, 64, 64)
Max pooling layer2 × 2, stride 1(1, 16, 32, 32)
Dropout layer-(1, 16, 32, 32)
Convolutional layer32 3 × 3 Convolution kernel(1, 32, 32, 32)
Max pooling layer2 × 2, stride 1(1, 32, 16, 16)
Dropout layer-(1, 32, 16, 16)
Fully connected layer512 maxout unit(32, 512)
Dropout layer-(32, 512)
Fully connected layerLogistic regression(32, 1)
Dropout layer-(32, 1)
Output layer-1