# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 3 Experimental Results
### Results  
The performance of the proposed two-stage classification method was compared to that of the conventional multiclass classification method, which simultaneously identified the given patches as normal tissue, MSI-L, or MSI-H. The representative cases of the patch-wise prediction results on the validation datasets were visualized with the heatmaps created for different image magnifications, as shown in Figures 4-6. Each patch in the heatmap was colored according to the prediction results; blue, red, and green patches represented the MSI status of tissues as MSI-L, MSI-H, and normal, respectively. The representative prediction results are shown in Figures 4 and 5, according to which both the proposed and conventional methods performed accurate prediction. Figure 4 shows the results for the MSI-H tissue. The results for the WSIs at 20\(\times\) magnification and 10\(\times\) magnifications are depicted in Figure 4b-d and 4e-g, respectively. In all cases, the network predicted MSI-H using the majority vote method from the patch-wise classification results. At 20\(\times\) magnification, the heatmaps created using the proposed method (Figure 4c) and the conventional method (Figure 4d) were similar. Most patches were identified as MSI-H within the segmented tumor area. In contrast, at the lower magnification, the conventional multiclass classification method resulted in a mixture of red and blue patches in the segmented tumor area (Figure 4g). Certain patches were even classified as normal tissue, depicted with green color in the heatmap. For the MSI-L tissue, the proposed and conventional methods performed accurate predictions at both 10\(\times\) and 20\(\times\) magnifications, as shown in Figure 5. However, at 10\(\times\) magnification, the proposed method did not precisely predict the status of the segmented tumor area, although the majority vote concluded the status as MSI-L (Figure 5f). Apparently, the proposed two-stage classification method performed better on 20\(\times\) magnified images. Another representative prediction result is shown in Figure 6. The figure shows the prediction results of the MSI-H tissue, wherein the proposed method and the conventional method predicted differently. The proposed method determined MSI-H tissues accurately; however, the conventional method identified them as MSI-L. The proposed method showed better performance in distinguishing the MSI-L and MSI-H status of the tumor patch, regardless of the image magnifications. Furthermore, the conventional method determined \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheduler Type** & \multicolumn{2}{c}{**Step Decay**} & \multicolumn{2}{c}{**Cosine Annealing**} \\
**(Option)** & **(Initial lr = 0.0001, Decayed by 10 Every 25 Epochs)** & **(Maximum lr = 0.0001, Epoch/Cycle = 20)** \\ \hline Optimizer & RMSProp & Adam & RMSProp & Adam \\ Precision & 0.93 & 0.96 & 0.95 & 0.96 \\ Recall & 0.93 & 0.96 & 0.95 & 0.96 \\ F1-score & 0.94 & 0.96 & 0.95 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: F1-score results with different optimizers and learning rate schedulers. several normal tissue patches as MSI-L, which led to an increase in the number of patches predicted as MSI-L, and thus, distorted the final decision. This was avoided with the proposed two-stage classification. The classification performance of the proposed method is presented with the F1-score, as shown in Table 2. The final decision obtained after the majority vote was used to describe the performance of the overall classification pipeline. The F1-score was used as the performance measure because of the general unequal distribution of data for MSI-L and MSI-H in clinical practice. The proposed method trained with the 20\(\times\) magnified images exhibited the highest F1-score of 0.83. In the case of both the proposed and the conventional methods, the network trained with the 20\(\times\) magnified images exhibited better performance than that trained with the 10\(\times\) magnified images.