## 2 Materials and Methods
### Deep Learning Approach
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{2}{c}{**Resnet-104**} & \multicolumn{2}{c}{**ResNet-68**} & \multicolumn{2}{c}{**DenseNet-71**} \\ \hline \multicolumn{2}{c}{Input layer: \(256\times 256\times 1\) grayscale image} \\ \hline L1 & \(\left[3\times 3,16\right]\) & Conv1 & \(\left[3\times 3,16\right]\) & Conv1 & \(\left[3\times 3,16\right]\) & Conv1 \\ \hline L2 & \(\left[\begin{array}{c}1\times 1,\ 8\\ 3\times 3,\ 8\\ 1\times 1,16\end{array}\right]\times 2\) & Conv2: \(\left[\begin{array}{c}1\times 3\times 3,\ 8\\ 3^{\text{\text{\text@underline{3}}}}2=6\\ \text{layers}\end{array}\right]\) & \(\left[\begin{array}{c}1\times 1,\ 8\\ 3\times 3,\ 16\end{array}\right]\times 1\) & Conv2: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 16\end{array}\right]\times 2\) & Conv2: \(\left[\begin{array}{c}2^{\text{\text@underline{2}}}2=4\\ 2^{\text{\text@underline{2}}}2=4\\ \text{layers}\end{array}\right]\) \\ \hline \multicolumn{2}{c}{Conv2+1 layer. Max pool \(\left[1\times 1,32\right]\)} \\ \hline L3 & \(\left[\begin{array}{c}1\times 1,\ 16\\ 3\times 3,\ 16\\ 1\times 1,32\end{array}\right]\times 2\) & Conv3: \(\left[\begin{array}{c}1\times 1,\ 16\\ 3\times 3,\ 16\\ 3^{\text{\text@underline{3}}}2=6\\ \text{layers}\end{array}\right]\times 1\) & Conv3: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 16\end{array}\right]\times 2\) & Conv3: \(\left[\begin{array}{c}2^{\text{\text@underline{2}}}2=4\\ 2^{\text{\text@underline{2}}}2=4\\ 2^{\text{\text@underline{2}}}2=4\\ \text{layers}\end{array}\right]\) \\ \hline \multicolumn{2}{c}{Conv3+1 layer. Max pool \(\left[1\times 1,64\right]\)} \\ \hline L4 & \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 32\\ 1\times 1,64\end{array}\right]\times 4\) & Conv4: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3^{\text{\text@underline{3}}}4=12\\ \text{layers}\end{array}\right]\times 2\) & Conv4: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 16\end{array}\right]\times 4\) & Conv4: \(\left[\begin{array}{c}2^{\text{\text@underline{2}}}4=8\\ 2^{\text{\text@underline{4}}}4=8\\ \text{layers}\end{array}\right]\) \\ \hline \multicolumn{2}{c}{Conv4+1 layer. Max pool \(\left[1\times 1,128\right]\)} \\ \hline L5 & \(\left[\begin{array}{c}1\times 1,\ 64\\ 3\times 3,\ 64\\ 1\times 1,128\end{array}\right]\times 4\) & Conv5: \(\left[\begin{array}{c}1\times 1,\ 64\\ 3\times 3,\ 64\\ 4=12\\ \text{layers}\end{array}\right]\times 1\) & Conv5: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 16\end{array}\right]\times 4\) & Conv5: \(\left[\begin{array}{c}2^{\text{\text@underline{2}}}4=8\\ 2^{\text{\text@underline{4}}}4=8\\ \text{layers}\end{array}\right]\) \\ \hline \multicolumn{2}{c}{Conv5+1 layer. Max pool \(\left[1\times 1,256\right]\)} \\ \hline L6 & \(\left[\begin{array}{c}1\times 1,\ 128\\ 3\times 3,\ 128\\ 1\times 1,256\end{array}\right]\times 6\) & Conv6: \(\left[\begin{array}{c}1\times 1,\ 128\\ 3^{\text{\text@underline{3}}}6=18\\ \text{layers}\end{array}\right]\times 3\) & Conv6: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 128\\ 1\times 1,256\end{array}\right]\times 3\) & Conv6: \(\left[\begin{array}{c}1\times 1,\ 32\\ 3\times 3,\ 16\end{array}\right]\times 6\) & Conv6: \(\left[\begin{array}{c}2^{\text{\text@underline{2}}}6=12\\ \text{layers}\end{array}\right]\) \\ \hline \multicolumn{2}{c}{Conv6+1 layer. Max pool \(\left[1\times 1,512\right]\)} \\ \hline L7 & \(\left[\begin{array}{c}1\times 1,\ 256\\ 3\times 3,\ 256\\ 1\times 1,512\end{array}\right]\times 6\) & Conv7: \(\left[\begin{array}{c}1\times 1,\ 256\\ 3^{\text{\text@underline{3}}}6=18\\ \text{layers}\end{array}\right]\times 1\) & Conv7: \(\left[\begin{array}{c}3\times 3,\ 256\\ 1\times 1,512\end{array}\right]\times 3\) & Conv7: \(\left[\begin{array}{c}3\times 3,\ 512\end{array}\right]\times 3\) & Conv7: \(\left[\begin{array}{c}3\times 3,\ 256\\ 3^{\text{\text@underline{3}}}3=9\\ \text{layers}\end{array}\right]\times 3\) & Conv3 layers \\ \hline \multicolumn{2}{c}{Upsampling layers} \\ \hline L8 & Deconvolution\(\left[2\times 2,512\right]\). Add L6 output before max pooling \(\left[3\times 3,256\right]\times 4\). 1+4 = 5 conv \\ & layers & & \\ \hline L9 & Deconvolution\(\left[2\times 2,256\right]\). Add L5 output before max pooling \(\left[3\times 3,128\right]\times 4\). 1+4 = 5 conv \\ & layers. & \\ \hline L10 & Deconvolution\(\left[2\times 2,128\right]\). Add L4 output before max pooling \(\left[3\times 3,64\right]\times 4\). 1+4 = 5 conv layers \\ & layers & \\ \hline L11 & Deconvolution\(\left[2\times 2,64\right]\). Add L3 output before max pooling \(\left[3\times 3,32\right]\times 4\). 1+4 = 5 conv layers \\ & layers & & \\ \hline L12 & Deconvolution\(\left[2\times 2,32\right]\). Add L2 output before max pooling \(\left[3\times 3,16\right]\times 4\). 1+4 = 5 conv \\ & layers. & & \\ \hline L13 & \(\left[1\times 1,1\right]\) Softmax. 1 convolutional layer \\ \hline \multicolumn{2}{c}{\(\Sigma\)} & **104** & **68** & **71** \\ \hline \hline \end{tabular}
\end{table}