Fig. 2. The principle of convolutional neural networks.

The input image is converted to numerical data (1–20) as the convolution layer. The convolutional neural network generates a pooling layer to reduce the dimensions of the image data as well as retain its characteristics for the statistic modeling. Several types of pooling methods including max pooling, which returns the maximum value from the portion of the image, and average pooling, which returns the average of all the values from the portion of the image. In addition, max pooling also performs de-noising along with dimensionality reduction, which improved analysis and accuracy.