uses stride$=2$ in the first convolutional layer instead of $2 \times 2$ max pooling to reduce feature map size at the beginning of the block, followed by the stacking of several convolutional layers. We use the “bottleneck design” [@he_deep_2015] which consists of repeated units of three convolutional layers that have filter sizes of $1 \times 1$, $3 \times 3$ and $1 \times 1$, respectively. A key feature of the Resnet block is that a shortcut is made between the two ends of each unit so that the features are directly carried over and therefore each unit can focus on learning the “residual” information [@he_deep_2015]. Batch normalization (BN) is used in every convolutional layer in the Resnet, which is known to speedup convergence and also has a regularization effect [@ioffe_batch_2015]. A Resnet block can be represented by the pattern of $[L-M-N] \times K$, where $L$, $M$ and $N$ represent the depths of the three convolutional layers in a unit and $K$ represents the number of units. Here, the 16-layer VGG network (VGG16) and the 50-layer Resnet (Resnet50) are used as patch classifiers. In the original design of the VGG16 [@simonyan_very_2014], it consists of five VGG blocks followed by two FC layers. 