Figure 2.

Overall architecture of Visual Geometry Group-16 model. Visual Geometry Group-16 (VGG-16) comprises five blocks and three fully connected layers. Each block includes convolutional layers followed by a max-pooling layer. Flattening of the output matrix after block 5 resulted in two fully connected layers for binary classification. The deep convolutional neural network used ImageNet parameters; the weights of blocks 1–4 were fixed, whereas the weights of block 5 and the fully connected layers were adjusted.