Figure 4

The T2D5 deep learning architecture. The model contained five convolutional layers (the numbers of each layer: 32, 64, 92, 100 and 128). Additionally, a Maxpooling layer was included after each convolution layer. Next, the outputs from convolutional layers were subjected to global average pooling and flattened for a fully connected layer (1028 nodes). Finally, a Softmax classifier, which applies a categorical cross-entropy loss function, was used.