. 2019 Oct 20;2019:7401235. doi: 10.1155/2019/7401235

Table 2.

Architecture of CNN-1 and CNN-2 in this study.

Layer	Input size	CNN-1	Input size	CNN-2
Conv_1	256 × 256 × 1	3 × 3, 32	256 × 256 × 1	3 × 3, 32
Dense-block_1	256 × 256 × 32	$1 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$	256 × 256 × 32	$1 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$
Max-pooling_1	256 × 256 × 64	2 × 2, stride 2	256 × 256 × 64	2 × 2, stride 2
Dense-block_2	128 × 128 × 64	$2 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$	128 × 128 × 64	$2 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$
Max-pooling_2	128 × 128 × 128	2 × 2, stride 2	128 × 128 × 128	2 × 2, stride 2
Dense-block_3	64 × 64 × 128	$4 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$	64 × 64 × 128	$4 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$
Max-pooling_3	64 × 64 × 256	2 × 2, stride 2	64 × 64 × 256	2 × 2, stride 2
Dense-block_4	32 × 32 × 256	$8 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$	32 × 32 × 256	$4 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$
Max-pooling_4	32 × 32 × 512	2 × 2, stride 2	32 × 32 × 384	2 × 2, stride 2
Dense-block_5	16 × 16 × 512	$16 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$	16 × 16 × 384	$4 \times \{\begin{matrix} 1 \times 1,128 \\ 3 \times 3,32 \\ concatenate \end{matrix}$
Average-pooling	16 × 16 × 1024	16 × 16	16 × 16 × 512	16 × 16
Fully connected layer	1024	2	512	2
Output	2		2

Here, “conv” denotes convolutional layer. Number formats of CNN-1 and CNN-2 are all: convolution kernel size, number of convolution kernels.