# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 3 Experimental Results
### Automated M-Mode Classification Results  
Table 2 shows an average sample classification performance over a ten-times-repeated fourfold stratified cross-validation with patient-wise split. We present six ResNets with varying input size. The number of frames ranges from 8 to 256. Therefore, the smallest input size is \(3\times 480\times 8\) and the largest is \(3\times 480\times 256\). Balanced accuracy ranges from 62% to 78%, with the best-performing model at 64 frames and the worst-performing model at 256 frames; 64-frame and 256-frame models are also the best- and the worst-performing, respectively, in sensitivity. On the other hand, the most specific model is the 128-frame version, with 83% specificity and 65% sensitivity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tissue** & **Ratio** & **Accuracy** & **Precision** & **Recall** & **IoU** \\ \hline Lung & 1:4.6 & 0.93 & 0.94 & 0.79 & 0.75 \\ Pleura & 1:36.2 & 0.98 & 0.75 & 0.71 & 0.61 \\ Rib & 1:4.4 & 0.90 & 0.93 & 0.72 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average performance of U-Net in binary segmentation of the lung, pleura, and rib. (Ratio: Target class:background; Acc. : Accuracy; IoU: Intersection over union. )The video aggregation results presented in Table 3 are an average of ten-times-repeated fourfold stratified cross-validation with patient-wise split. Six ResNets were trained with varying numbers of input frames; their sample classification performance is available in Table 2. Two types of averaging aggregation are performed: Volume and video aggregation. Volume aggregation is either performed within all slices of a lung or within three slices in a single volume; see Section 2.2.2 for details. Video aggregation puts these predictions together with a fixed step of eight frames for the whole duration of the video sample. The step size is the minimal number of frames (N = 8) to minimise the information loss during aggregation. This approach produces pairs of classifiers that only differ in terms of the selection of the input slices from the input volume. With this test design, we can perform paired tests between an 8-frame ResNet 18 with lung slice selection and the same 8-frame ResNet 18 with centroid slice selection; between a 16-frame ResNet 18 with lung slice selection and a 16-frame ResNet 18 with centroid slice selection; and so on. Our null hypothesis is that the balanced accuracy means between the pairs of classifiers are equal. The alternative hypothesis is that the balanced accuracy means are not equal. We use estimation statistics to compare the mean differences between our classifier pairs [19]. The reported \(p\)-values of the two-sided permutation \(t\)-test are the likelihoods of observing the effect sizes if the null \begin{table}
\begin{tabular}{c c c c} \hline \hline
**N** & **Bal. Accuracy** & **Sensitivity** & **Specificity** \\ \hline
8 & 0.71 \(\pm\) 0.07 & 0.65 \(\pm\) 0.17 & 0.76 \(\pm\) 0.16 \\
16 & 0.76 \(\pm\) 0.08 & 0.76 \(\pm\) 0.14 & 0.75 \(\pm\) 0.16 \\
32 & 0.77 \(\pm\) 0.08 & 0.77 \(\pm\) 0.15 & 0.77 \(\pm\) 0.17 \\
64 & 0.78 \(\pm\) 0.07 & 0.78 \(\pm\) 0.14 & 0.78 \(\pm\) 0.14 \\
128 & 0.74 \(\pm\) 0.11 & 0.65 \(\pm\) 0.21 & 0.83 \(\pm\) 0.14 \\
256 & 0.62 \(\pm\) 0.15 & 0.44 \(\pm\) 0.35 & 0.81 \(\pm\) 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average M-mode image classification performance metrics with standard deviations of ResNet18 with different numbers of input frames. (N: Number of frames; Slice sel. : Slice selection method according to Section 2.2.2; Bal. Accuracy: Balanced accuracy.)