## Appendix A  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Citeria** & **SD 2-Class** & **SD 4-Class** & **NSD** \\ \hline Accuracy (\%) & 98.9 & 94.5 & 99.7 \\ Sensitivity & 0.98 & 0.94 & 0.99 \\ Specificity & 0.98 & 0.98 & 0.99 \\ Precision & 0.98 & 0.94 & 0.98 \\ F-score & 0.98 & 0.94 & 0.98 \\ Performance improvement (\%) & 1.43 & 7.0 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the experimental results of the proposed method.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer** & **Layer Order** & **Layer** & **Layer Info** \\ \hline
6 & ‘bathnorm\_2’ & Batch Normalization & Batch normalization with 8 channels \\
7 & ‘relu2’ & ReLU & ReLU \\
8 & ‘maxpool2d2’ & 2D Max Pooling & In total, 2 \(\times\) 2 max pooling with stride [1 - 1] and padding [0 0 0 0] \\
9 & ‘conv2d\_2’ & 2D Convolution & In total, 8 2 \(\times\) 2 \(\times\) 8 convolutions with stride [1 - 1] \\
10 & ‘relu\_2\_1\_1\_5’ & ReLU & ReLU \\
11 & ‘conv2d\_3’ & 2D Convolution & In total, 8 3 \(\times\) 3 \(\times\) 8 convolutions with stride [1 - 1] \\
12 & ‘maxpool2d\_2’ & 2D Max Pooling & In total, 2 \(\times\) 2 max pooling with stride [1 - 1] and padding [0 0 0 0] \\
13 & ‘conv2d2\_2’ & 2D Convolution & In total, 8 5 \(\times\) 5 \(\times\) 8 convolutions with stride [1 - 1] \\
14 & ‘relu\_2\_1\_1\_6’ & ReLU & ReLU \\
15 & ‘conv2d2\_3’ & 2D Convolution & In total, 8 3 \(\times\) 3 \(\times\) 8 convolutions with stride [1 - 1] \\
16 & ‘sigmoid\_1\_1\_1\_3’ & sigmoidLayer & sigmoidLayer \\
17 & ‘mul\_1\_1\_1\_3’ & ElementWiseMultiplication & Element-Wise Multiplication of 2 inputs \\
18 & ‘fc0’ & Fully Connected & In total, 350 fully connected layers \\
19 & ‘ReLu2’ & ReLU & ReLU \\
20 & ‘input’ & Image Input & In total, 32 \(\times\) 200 \(\times\) 3 images with ‘zero center’ \\
21 & ‘convInp’ & 2D Convolution & In total, 16 3 \(\times\) 3 \(\times\) 3 convolutions with stride [1 - 1] \\
22 & ‘BNInp’ & Batch Normalization & and padding ‘same’ \\
23 & ‘reluInp’ & ReLU & Batch normalization with 16 channels \\
24 & ‘S1U1 conv1’ & 2D Convolution & In total, 16 3 \(\times\) 3 \(\times\) 16 convolutions with stride [1 - 1] \\
25 & ‘S1U1 BN1’ & Batch Normalization & Batch normalization with 16 channels \\
26 & ‘S1U1 relu1’ & ReLU & ReLU \\
27 & ‘S1U1 conv2’ & 2D Convolution & ReLU \\
28 & ‘S1U1 BN2’ & Batch Normalization & In total, 16 3 \(\times\) 3 \(\times\) 16 convolutions with stride [1 - 1] \\
29 & ‘add11’ & Addition & Element-wise addition of 2 inputs \\
30 & ‘relu11’ & ReLU & ReLU \\
31 & ‘S1U2 conv1’ & 2D Convolution & In total, 16 3 \(\times\) 3 \(\times\) 16 convolutions with stride [1 - 1] \\
32 & ‘S1U2 BN1’ & Batch Normalization & Batch normalization with 16 channels \\
33 & ‘S1U2 relu1’ & ReLU & ReLU \\
34 & ‘S1U2 conv2’ & 2D Convolution & In total, 16 3 \(\times\) 3 \(\times\) 16 convolutions with stride [1 - 1] \\
35 & ‘S1U2 BN2’ & Batch Normalization & Batch normalization with 16 channels \\
36 & ‘add12’ & Addition & Element-wise addition of 2 inputs \\
37 & ‘relu12’ & ReLU & ReLU \\
38 & ‘S2U1 conv1’ & 2D Convolution & In total, 8 3 \(\times\) 3 \(\times\) 8 convolutions with stride [1 - 1] \\
39 & ‘S2U1 BN1’ & Batch Normalization & Batch normalization with 32 channels \\
40 & ‘S2U1 relu1’ & ReLU & ReLU \\
41 & ‘S2U1 conv2’ & 2D Convolution & In total, 32 \(\times\) 3 \(\times\) 32 convolutions with stride [1 - 1] \\
42 & ‘S2U1 BN2’ & Batch Normalization & Batch normalization with 32 channels \\
43 & ‘add21’ & Addition & and padding ‘same’ \\
44 & ‘relu21’ & ReLU & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**CNN Model** & **Model File Size (MB)** \\ \hline ResNet & 93.2 \\ VGGNet & 502.9 \\ AlexNet & 222.03 \\ MobileNet & 12.95 \\ PARC Model & 64.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sizes of the PARC model and popular pre-trained CNN models.