# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 3 Results
### Ablation Study  
We executed several experiments before we arrived at the final model, data processing, and parameter choices. In this subsection, we briefly summarize our findings. We cover the three main aspects of the algorithm:  
1. Model selection;
2. Data preprocessing;
3. Hyper-parameter setting. The above order does not represent the order of our experiments. It was established in order to explain our experience in more logical fashion. We did not measure every possible combination of choices; therefore, we can explain and showcase the tendencies of the different choices. Model selection. We tried three main architectures. The first architecture was a fully convolutional model with 4-5 convolutional layers, assuming the ensemble model with more views can achieve good results overall and we would not need strong learners per view. Our results indicated that bigger networks would be required to achieve scores (accuracy, F1-score, etc.) around 90 percent. The second architecture was similar to ResNet with two-dimensional convolutions. The time dimension in the long-axis view was stacked together to form a 12-channel image. The structure was similar to the ResNet described in Section 2.2. We experienced significant performance growth (around 3-4 percent) as the model size achieved 8 residual blocks, meaning 16 convolutional layers overall. Further increasing the size did not affect performance significantly. One reason for that may be the size of the dataset. During the data-preprocessing-related changes, we came to the conclusion that taking into account the time dimension (basically the movement or dynamic patterns of the heart) had a major effect on the results (over six percent in the case of the short-axis views). Therefore, we created a 3D convolution-based ResNet model to properly handle the time dimension. We formed a 3D image, as time became the depth dimension of the image. This model performed better and more robustly (regarding the sensitivity for the hyper-parameters). However, the drawback of the 3D ResNet lies in its slow training  
Figure 6: Comparison of the human (expert) and algorithm (auto) performances. High recall is beneficial because the algorithm can identify samples suspicious of hypertrophy with a high probability. The false positives can be handled by the experts who supervise the examination. The \(p\)-value is less than 0.001 between the auto (LA) and auto (SA+LA) groups. When comparing auto (SA+LA) and the expert groups, we obtained a \(p\)-value = 0.3, indicating there was no statistically significant difference between them. Therefore, the auto (SA+LA) was statistically identical to the expert group in terms of the recall. speed. As the performance on the short-axis view was worse, we tried to increase the model size for this view only, but this did not cause relevant changes. Finally, we used the same architecture for all the views. Data preprocessing. Data preprocessing and the input representation to the network proved to be the most important factors. To speed up the training, we tried less input data first. We used only two images from the long-axis views, one from the systole phase and one from the diastole phases. We used six images from the short-axis view and three images at the systole and the diastole phases, respectively. This input formation resulted in fair accuracy values (around 84 percent), but it turned out that taking images from other points of the cardiac cycle contributed to better results. Standardization (see Appendix A) had a very important role in achieving the final results. We identified the long-axis views to be noisy as a result of the different orientations of the images. This was not true for the short-axis. One way to cope with this is to use random rotation for augmentation with degrees between 0 and 180. We found this approach to be inefficient in helping the learning process. The standardization method caused a significant performance growth. Therefore, we used only a small eight-degree angle for rotation during augmentation.