## 3 Results
### Testing Set Prediction  
Build the final model after finding the most optimal hyper-parameters, the weights of the prediction model are decided by running through the training and validation sets. The 3D convolutional neural network model structure is shown in Figure 6. The iteration process curve is shown in Figure 7. Since we use the five-fold cross-validation method to check model robustness, five execution results can be obtained in five iterations. We have plotted the loss curves and accuracy curves for validation sets in five iterations in Figure 7, [val_loss1, val_loss2, val_loss3, val_loss4, val_loss5] and [val_acc1, val_acc2, val_acc3, val_acc4, val_acc5]. The mean curves of five iterations in validation sets and training sets are also plotted in Figure 7, [val_loss_mean, train_loss_mean] and [val_acc_mean, train_acc_mean]. The loss of training and validation declines steadily. The accuracy of training and validation rises steadily. It stops iterating before overfitting. Finally, we evaluate the performance of this determined prediction model for the specified test set. The test set is the same for different runs. The prediction result for comparing image preprocessing steps of testing set is that shown in Table 8. The confusion matrix with combining three steps of image preprocessing is shown in Table 9. There are 4 normal cases that are miss-classified as abnormal. Also, there are 7 abnormal cases that are miss-classified as normal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activation** & **Sigmoid** & **Tanh** & **Relu** \\ \hline Mean & 0.6461 & 0.7809 & 0.7899 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result of the Activation Function Selection.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pooling** & **1\(\times\) 1\(\times\) 1** & **1\(\times\) 1\(\times\) 2** & **1\(\times\) 1\(\times\) 3** & **2\(\times\) 2\(\times\) 1** & **2\(\times\) 2\(\times\) 2** & **2\(\times\) 2\(\times\) 3** & **3\(\times\) 3\(\times\) 1** & **3\(\times\) 3\(\times\) 2** & **3\(\times\) 3\(\times\) 3** \\ \hline Mean & 0.7539 & 0.7641 & 0.7685 & 0.7876 & 0.7708 & 0.7573 & 0.7764 & 0.7629 & 0.7719 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of Pooling Size Selection.  
Figure 6: 3D Convolutional Neural Network Model Structure.  
The testing set is processed by image cropping 1, image selection, and image cropping 2. The prediction accuracy gradually rises. The result of combining three processing is better than the others that prove the importance of image preprocessing. The mean of accuracy, sensitivity and specificity is 0.8764, 0.8158, and 0.9216. Lower sensitivity means that some unhealthy subjects cannot be detected that need to be solved in future works.  
The process of judging myocardial defects through models will be shown by grad-cam heat maps. From the convolutional neural network model architecture, return the filter value in the convolutional layer through the backpropagation operation. A heat map matrix is obtained after adding the filter by weight. Overlay heat map to the original image for visualization. The 15 grad cam maps for each subject are presented separately. For normal images, the model focuses on continuous areas that are shown in Figure 8. For abnormal images, the model focuses on both sides of the myocardial defect that are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Preprocessing** & **No Processing** & **Only Image** & **Image Cropping1 and** & **Combine Three** \\  & & **Cropping1** & **Image Selection** & **Processing** \\ \hline Subject & 89 & 89 & 89 & 89 \\ \hline Accuracy & 0.5730 & 0.7079 & 0.7753 & 0.8764 \\ \hline Sensitivity & 0.0000 & 0.6579 & 0.6053 & 0.8158 \\ \hline Specificity & 1.0000 & 0.7451 & 0.9020 & 0.9216 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Result of the Preprocessing Method.  
Figure 7: Iteration Process Curve with Combine Three Preprocessing.