## 3 Results  
### Training and Evaluation of the Classification  
Training and evaluation were conducted by using four-fold cross-validation. The highest mean (\(\pm\) standard deviation) ACA obtained for the four cross-validations was 94.3 \(\pm\) 2.1%. Similarly the best values for recall, precision, specificity, and F-measure were 93.8 \(\pm\) 2.2%, 94.9 \(\pm\) 3.3%, 94.9 \(\pm\) 3.0%, and 94.3 \(\pm\) 2.1%, respectively. When we compared these evaluation metric values with those of the model developed by [PERSON] et al. (2016) (Table 2, 'Baseline'), which used the same training and evaluation methods and was trained using dataset that we used in the present study, all four of our model's evaluation metrics exceeded those of the model of [PERSON] et al. (2016)  
Our model achieved a high ACA of 94.3%, which suggested that our trained neural network could be used to determine the informative features of neural differentiation. To determine whether the misprediction of differentiated cells as undifferentiated cells (FN) was more frequent than that of undifferentiated cells as differentiated cells (FP), a confusion matrix was created using the CNN model with the highest cross-validated ACA (Figure 3). The resultant numbers of FP and FN were 1 and 2, respectively, demonstrating that our model was more inclined to interpret differentiated cells as undifferentiated cells.  
To do this, we first analyzed the feature maps in our trained model. The distributions of the feature maps output from the third max pooling layer and the fully connected layer for all of the phase-contrast photomicrographs were visualized in the two-dimensional space by using t-SNE (Figure 4). The result  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Ours** & **Baseline** \\ \hline ACA & 94.3 \(\pm\) 2.1\% & 83.8 \(\pm\) 4.6\% \\ Recall & 93.8 \(\pm\) 2.2\% & 81.2 \(\pm\) 1.1\% \\ Precision & 94.9 \(\pm\) 3.3\% & 86.3 \(\pm\) 8.5\% \\ Specificity & 94.9 \(\pm\) 3.0\% & 85.2 \(\pm\) 5.0\% \\ F-measure & 94.3 \(\pm\) 2.1\% & 83.5 \(\pm\) 3.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of evaluation metrics for our model and those of the model of [PERSON] et al. (2016) (‘Baseline’).  
Figure 3: Confusion matrix for model achieving the highest accuracy (ACA) from cross validation (best fold model).  
from the distribution output from the third max pooling layer revealed two distinct clusters, one each for 'differentiated' and 'undifferentiated' (Figure 4a). The result obtained from the fully connected layer showed that the two clusters could be separated linearly (Figure 4b).  
We compared the model complexity between the baseline and our model. Previous research showed that model complexity could be evaluated by the number of multiply-accumulations (MACC). We calculated the MACC of the baseline model as 249,380,864 operations, whereas the MACC of our model was 5,611,902,462. This result indicates that the MACC of our model was approximately 20 times higher than that of the baseline model. The higher MACC of our models, and therefore, the higher model complexity, could be one of the reasons why the classification accuracy of our model was superior than that of the baseline model. We also confirmed that the execution of classification by our model took much more time than by the baseline model, and considered that this could be related to the increase of model complexity for our model.