# Deep Learning for Laryngopharyngeal Reflux Diagnosis
## 3 Results
### Backbone Network with Attention Modules
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Size** & **ResNet18** & **ResNet18\_SE** & **ResNet18\_CBAM** & **ResNet50** & **ResNet101** \\ \hline \(112\times 112\) & & & \(7\times 7\), 64, stride 2 & \\ \hline \(56\times 56\) & & max pooling, 3 \(\times 3\), stride 2 & \\ \cline{2-5}  & \(\left[\begin{array}{c}3\times 3,\ 64\\ 3\times 3,\ 64\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 64\\ 3\times 3,\ 64\\ f_{C},\ [4,\ 64]\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 64\\ f_{C},\ [4,\ 64]\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 64\\ f_{C},\ [4,\ 64]\end{array}\right]\times 2\) & \(\left[\begin{array}{c}1\times 1,\ 64\\ 3\times 3,\ 64\\ 1\times 1,\ 256\end{array}\right]\times 3\) \\ \hline \(28\times 28\) & \(\left[\begin{array}{c}3\times 3,\ 128\\ 3\times 3,\ 128\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 128\\ f_{C},\ [8,\ 128]\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 128\\ f_{C},\ [8,\ 128]\\ 7\times 7\end{array}\right]\times 2\) & \(\left[\begin{array}{c}1\times 1,\ 128\\ 3\times 3,\ 128\\ 1\times 1,\ 512\end{array}\right]\times 4\) & \(\left[\begin{array}{c}1\times 1,\ 128\\ 3\times 3,\ 128\\ 1\times 1,\ 512\end{array}\right]\times 4\) \\ \hline \(14\times 14\) & \(\left[\begin{array}{c}3\times 3,\ 256\\ 3\times 3,\ 256\\ f_{C},\ [16,\ 256]\\ 2\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 256\\ f_{C},\ [16,\ 256]\\ 2\end{array}\right]\times\) & \(\left[\begin{array}{c}3\times 3,\ 256\\ f_{C},\ [16,\ 256]\\ 7\times 7\end{array}\right]\times\) & \(\left[\begin{array}{c}1\times 1,\ 256\\ 3\times 3,\ 256\\ 1\times 1,\ 1024\end{array}\right]\times 6\) & \(\left[\begin{array}{c}1\times 1,\ 256\\ 3\times 3,\ 256\\ 1\times 1,\ 1024\end{array}\right]\times 2\) \\ \hline \(7\times 7\) & \(\left[\begin{array}{c}3\times 3,\ 512\\ 3\times 3,\ 512\end{array}\right]\times 2\) & \(\left[\begin{array}{c}3\times 3,\ 512\\ f_{C},\ [32,\ 512]\\ 2\end{array}\right]\times\) & \(\left[\begin{array}{c}3\times 3,\ 512\\ f_{C},\ [32,\ 512]\\ 7\times 7\end{array}\right]\times\) & \(\left[\begin{array}{c}1\times 1,\ 512\\ 3\times 3,\ 512\\ 1\times 1,\ 2048\end{array}\right]\times 3\) & \(\left[\begin{array}{c}1\times 1,\ 512\\ 3\times 3,\ 512\\ 1\times 1,\ 2048\end{array}\right]\times 3\) \\ \hline \(1\times 1\) & & average pooling, 1D fc, softmax & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The architectures of main backbone models. Figure 3 depicted the accuracy curves during training procedures. The left subfigure compared the performance of different depth. We could see that with more epochs the training accuracy of each model keeps improving, while the test accuracy starts to fluctuate from 150 epochs. Moreover, ResNet50 and RetNet18 performed similarly on test data at most epochs, but they offered higher accuracy than ResNet101. The right subfigure shows the training procedures of ResNet18 with and without attention modules. Clearly the ResNet18 with CBAM attention modules achieved the best test accuracy when compared to the ResNet18 backbone and the ResNet18 with SE attention modules. According to the above observations, the ResNet18 was chosen as our backbone network in the following, since it was lightweight and performed decently compared with other deep networks.