## Materials and Methods
### Post-Processing  
The similar performance of the CNN architectures in the normal/abnormal classification case is attributed to the descriptive properties of the data. Many A-lines were correctly classified as normal or abnormal with different CNN architectures. On the other hand, a smaller percentage of A-lines are misclassified. The test set includes information about the adjacency of A-lines. Based on the minimum extent of plaques, which is 40 A-lines, and the fact that a single normal A-line cannot reside inside a plaque region, it is assumed that an individual A-line, which is not classified as belonging to the same class with most of a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Name** & **Type** & **Activations** & **Learables** \\ \hline
1 & \(90\times 1\times 3\)images with “zerocenter”normalization & Image input & \(90\times 1\times 3\) & - \\
**Conv\_1** & & & & & \\
2 & \(32\times 1\times 3\) convolutions with stride [1 ] and padding [0 0 0 0] & Convolution & \(87\times 1\times 32\) & Weights \(4\times 1\times 3\times 32\) \\
3 & LeakyReLU\_1 & Leaky ReLU & \(87\times 1\times 32\) & - \\  & LeakyReLU with scale 0.01 & Leaky ReLU & \(87\times 1\times 32\) & - \\
4 & \(32\times 1\times 32\) convolutions with stride [1 ] and padding [0 0 0 0] & Convolution & \(86\times 1\times 32\) & Weights \(2\times 1\times 32\times 32\) \\
5 & LeakyReLU\_2 & Leaky ReLU & \(86\times 1\times 32\) & - \\  & LeakyReLU with scale 0.01 & Conv\_3 & Conv\_3 & Weights \(10\times 1\times 32\times 32\) \\
6 & \(32\times 1\times 32\) convolutions with stride [1 ] and padding [0 0 0 0] & Convolution & \(77\times 1\times 32\) & Bias \(1\times 1\times 32\) \\
7 & LeakyReLU\_3 & Leaky ReLU & \(77\times 1\times 32\) & - \\  & LeakyReLU with scale 0.01 & Leaky ReLU & \(77\times 1\times 32\) & - \\
8 & Fe\_1 & & & & Weights \(5\times 2464\) \\
8 & 5 fully connected layers & Fully Connected & \(1\times 1\times 5\) & Bias \(5\times 1\) \\
9 & fc\_2 & Fully Connected & \(1\times 1\times 2\) & Weights \(2\times 5\) \\
9 & 2 fully connected layers & Fully Connected & \(1\times 1\times 2\) & Bias \(2\times 1\) \\
10 & Softmax & Softmax & \(1\times 1\times 2\) & - \\  & softmax & Softmax & \(1\times 1\times 2\) & - \\
11 & **Classoutput** & Classification Output & - & - \\  & crossentropyvex with classes “1” and “2” & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elements of the proposed CNN architecture.  
Figure 4: Graphical illustration of the proposed CNN architecture. The network consists of 3 convolution and 2 fully connected layers.  
group of 20 adjacent A-lines, is misclassified. Therefore, a post-processing step to smooth classification results and correct misclassifications was introduced.  
Specifically, let the notation of classification be: C(i) = 0 (normal) or 1 (plaque), where i is the A-line number. For i = 1, 21, 42,..., M (where M is the total number of A-lines in an image): C(ii + 20) values are updated to 0 if \(\Sigma\)(C(ii + 20) \(\leq\) 10 and C(ii + 20) are update to 1 if \(\Sigma\)(C(ii + 20) \(>\) 10. This step decides the classification result of an A-line based on the dominant class of the classification results of adjacent A-lines. This produces a more realistic outcome, and it can improve test accuracy by 1-2% in most cases. An example of a post-processing result is shown in Figure 5.