Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning

Figure 4

General structure of CNN. The input layer is 32 ∗ 32. The input is convoluted to six feature maps in the C1 layer by 5 ∗ 5 convolution kernel. S2 is a pooling layer with six 14 ∗ 14 features. Each unit in the feature map is connected to the 2 ∗ 2 neighborhood of the corresponding feature map in the C1 layer. The C3 layer is also a convolutional layer that uses a kernel of 5 × 5 to convolute the layer S2. The S4 layer is a pooling layer that consists of sixteen 5 ∗ 5 size feature maps. The C5 layer is a convolutional layer with 120 feature maps. The F6 layer has 84 units and is fully connected to the C5 layer. The output layer has a unit with 84 inputs.