CNN is commonly accompanied by fully connected layers and dropout layers. A fully connected layer, also called a dense layer, is the basic component of the neural network. It takes the features extracted by the convolution layers as the input and maps them to the final output with some linear transformation. As for the dropout layer, it is a layer to prevent our model from overfitting, a phenomenon that the model considers the noise as a feature of the data by randomly setting some neurons freeze with a certain possibility. For improving the accuracy of image classification, we reserved the original CNN model mentioned above and appended VGG16, another model architecture, and transferred learning to it to improve the accuracy of the AI model and speed up the training time of the AI model. The details of the AI model building can be found in SI-6.