# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 4 Discussion and Conclusions  
A typical multiclass classification method categorizes the three MSI status simultaneously; however, such a method exhibits poor efficiency in distinguishing MSI-H from other tissue types. In this paper, we present a novel two-stage classification pipeline to predict the MSI status of CRC tissue. The proposed classification pipeline serially connected two classifiers: the segmentation network and the classification network. The segmentation network, in the first stage, prevented the incorrect prediction of normal tissues as MSI-L by excluding them in advance; this confusion was, however, observed when using the multiclass classification method. The performance of the proposed method was compared to that of the multiclass classification method using the F1-score as the evaluation measure. The proposed method notably outperformed the one multiclass classification. In this approach, the binary classification was performed in the region only determined as tumor tissue using the prior segmentation techniques, which enables to perform the classification on a dataset with significantly reduced imbalance. Particularly, the given original dataset we used for this study was unbalanced; for example, in each WSI, the normal tissue occupied more regions than the tumor tissue. Since we could exclude the region of the normal tissues at the first segmentation stage, the binary classification at the second stage could result in better performance. The results of our classification method were comparable to that of the eighth-place team for PAIP 2020 competition; however, the direct comparison was hard to be evaluated \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Magnification** & \multicolumn{2}{c}{**20\(\times\)**} & \multicolumn{2}{c}{**10\(\times\)**} \\ \hline \multirow{2}{*}{**Method**} & **Two-Stage** & **Conventional** & **Two-Stage** & **Conventional** \\  & **Classification** & **Classification** & **Classification** & **Classification** \\ \hline Precision & 0.83 & 0.67 & 0.83 & 0.67 \\ Recall & 0.83 & 0.80 & 0.71 & 0.67 \\ F1-score & 0.83 & 0.73 & 0.77 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the proposed two-stage classification method compared to the conventional method on images at 20\(\times\) and 10\(\times\) magnifications. Figure 6: Patch-wise classification results of the MSI-H tissue, which were predicted as MSI-H for the proposed method but MSI-L for the conventional method. (**a**) The original WSI. The segmented tumor mask and the heatmap created using the proposed method and the conventional method with images at 20\(\times\) magnification (**b–d**) and 10\(\times\) magnification (**e–g**). since the dataset we used for this study was different to that used for the final rank. The first-place team adopted the sequentially connected segmentation and classification network; after the segmentation stage, one hundred patches were randomly selected and VGG16 was applied to each patch followed by a recurrent neural network (RNN). The team scored the F1-score of 0.92 using the test dataset, which is 0.09 higher than that of our approach. The required high computational power and complexity of the network pipeline may lead to better performance. Our study rather focuses on experimentally verifying the performance improvement of using two-stage classification compared to the one conventional multiclass classification, although different networks for the segmentation and classification in two-stage classification pipeline may result in better performance. Furthermore, we have identified the image magnification effect on the F1-Score. Both the proposed and the conventional method showed better performance with images at 20\(\times\) than at 10\(\times\) magnification. In general, as the magnification increased, information about the neighboring cells disappeared; however, the geometrical characteristics of each cell, such as size, convexity, and intervals between cells, were emphasized.