# Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data
## 3 Results  
The ground truth for the videos is a list of time segments where an abnormality was observed and annotated by an expert. An abnormality may be visible for as short as a few seconds to as long as several minutes. We considered an abnormal segment detected if at least one frame from the segment is predicted as abnormal. In our first experiment, we trained the deep learning model detailed in Figure 3, for ten epochs, using the final epoch to make predictions on the test videos. The results for the three videos are shown in Table 1. This single network was able to identify 21 of the 119 abnormalities, while reducing the number of frames on average by 97%. Though this method produces a sizeable reduction in video length, the number of abnormalities detected is less than 18%. In real world applications, there will always be a physician to look over the predictions; thus, normal frames being incorrectly classified as abnormal is acceptable. However, incorrectly classifying abnormal frames as normal could have dire consequences for patient outcomes. Therefore, our goal is to limit the number of missed abnormal segments. Since the single model did not perform well, we decided to use an ensemble approach to increase performance. As discussed above, we decided to save intermediate models while training to have them vote on predictions. In our second experiment, we again trained the model described in Figure 3 for ten epochs; however, we saved each of the ten intermediate models. We then had the first nine intermediate models make a prediction for each frame in the videos. Since the most important aspect is not to miss an abnormality, we decided on a voting scheme where all of the nine networks had to agree on a normal frame for it to be given that label. A paired \(t\)-test with a confidence interval of 95% showed a statistically significant improvement in the detection of abnormal segments with this ensemble in the three-fold cross validation over a single model as seen in Table 2 below. As shown in Table 3, the ensemble method was able to identify 85 of the 119 abnormal segments, while reducing the number of frames by 75%. The voting was repeated using the last nine models, and it was found to perform marginally worse with only 80 of the 119 abnormal segments detected, and a reduction in number of frames by 76%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video Name** & **Total Frames** & **Number of Abnormal Abnormal Segments** & **Reduced Frames Frames** & **Abnormal Segments Detected** \\ \hline KID Video 1 & 28,480 (95 min) & 22 & 616 (2 min) & 3 \\ KID Video 2 & 117,565 (391 min) & 86 & 956 (3 min) & 10 \\ KID Video 3 & 74,762 (249 min) & 11 & 4522 (15 min) & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Single Network Trained on Non-Augmented Videos. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **n** & **Mean** & **Variance** & **t-calc** & **t-crit** & **df** & \(\mathbf{p}\) \\ \hline Single Network & 119 & 0.176 & 0.147 & 11.72 & 1.98 & 118 & \(1.66\times 10^{-21}\) \\ Ensemble No Aug & 119 & 0.714 & 0.206 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Paired \(t\)-test Results Comparing Accuracy of Single Network vs. Ensemble with No Augmentation. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of Abnormal Segments** & **Reduced Frames Frames** & **Abnormal Segments Detected** \\ \hline KID Video 1 & 28,480 (95 min) & 22 & 10,747 (36 min) & 21 \\ KID Video 2 & 117,565 (391 min) & 86 & 10,960 (37 min) & 53 \\ KID Video 3 & 74,762 (249 min) & 11 & 33,963 (113 min) & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ensemble of Networks Trained on Non-Augmented Videos. This method performed very well on videos 1 and 3 but missed a large number of abnormal segments on video 2. We hypothesized this was due to the number of abnormal examples in the training set.