by one or more fully connected (FC) layers to join with the classification output. Max pooling layers are often used amid convolutional layers to achieve translational invariance and to reduce feature map size. In this study, two popular CNN structures are compared: the VGG network [@simonyan_very_2014] and the residual network (Resnet) [@he_deep_2015]. Consecutive network layers can be naturally grouped into “blocks” so that the feature map size is reduced (typically by a factor of 2) either at the beginning or at the end of a block but stays the same elsewhere in the block. For example, a “VGG block” is a stack of several $3 \times 3$ convolutional layers with the same depth followed by a $2 \times 2$ max pooling layer that reduces the feature map size by a factor of 2. Although other filter sizes can be used, $3 \times 3$ convolution and $2 \times 2$ max pooling seem to be the most popular choices and are used throughout this study unless otherwise stated. Therefore, a VGG block can be represented by the pattern of $N \times K$, where $N$ represents the depth of each convolutional layer and $K$ represents the number of convolutional layers. A “Resnet block” 