## 4 Discussion  
Image resolution is considered as one factor which influences classification accuracy in bio-image analysis. To confirm the optimal image resolution to distinguish single differentiated or undifferentiated cell, we compared results obtained from (i) the original image resolution of 400 \(\times\) 400 pixels, and reduced image resolutions of (ii) 200 \(\times\) 200 pixels and (iii) 100 \(\times\) 100 pixels by performing model learning and confirming classification accuracy. The ACA obtained from the original (400 \(\times\) 400) and reduced resolution images (200 \(\times\) 200, 100 \(\times\) 100) were 94.9 \(\pm\) 1.9%, 94.3 \(\pm\) 2.1% and 86.4 \(\pm\) 2.1%, respectively. Whereas the ACA obtained for 400 \(\times\) 400 and 200 \(\times\) 200 pixel images were within the standard deviation, the ACA obtained for the 100 \(\times\) 100 image was considerably smaller than that obtained for the 200 \(\times\) 200 pixel image. This indicates the model learning performs similarly for 400 \(\times\) 400 and 200 \(\times\) 200 pixel resolution images and does not affect the ACA classification of differentiated and undifferentiated cells. In terms of model complexity however, the MACC obtained for 400 \(\times\) 400 pixel images was 14,247,901,062, which was 2.5 times higher than the MACC obtained for 200 \(\times\) 200 pixel image. These results support the validity of using 200 \(\times\) 200 down-scaled resolution used in our study to distinguish differentiated and undifferentiated cells. Our CNN model achieved an ACA of 94.3% and an F-measure of 94.3%; these values are higher than those reported previously by [PERSON] et al. for a similar model [16]. Comparing the features learned by our model and those learned by the model of [PERSON] et al., we visualized the feature map output from the pooling layer of both models using the same image of a differentiated SH-SYSY cell (Figure 8a). The feature map produced by the model of [PERSON] et al. showed that only the halo region was considered an informative feature for the differentiated cell (Figure 8b). In contrast, the feature map produced by our model showed three informative features: halo regions, elongated neurites, and the shape of the edge of the cell (Figure 8c). The reason our model afforded more informative features than did the model of [PERSON] et al. could be because the hyperparameters of our model, such as the size of the filter used in the convolution layer and the stride width, were optimized and thus prevented our model from missing informative features. The higher accuracy and F-measure produced by our model compared with that of [PERSON] et al. could then be explained by the greater number of informative features learned. Of the extracted informative features for differentiated SH-SYSY cells, a strong halo of bright light is generally observed in phase-contrast photomicrographs after the induction of differentiation [18], and neurites are well-known to elongate during differentiation. Thus, based on our present knowledge, the informative features learned by our model were biologically relevant to differentiated SH-SYSY cells. As informative features for undifferentiated cells, short dendrites and the shape of the edge of the cell were learned by our model. A previous study reported that the dendrites of SH-SYSY cells after exposure to 10 \(\upmu\)M of all-trans retinoic acid remained immature with a length of 10-40 \(\upmu\)m [29]. In addition, it has been confirmed that during neurite elongation actin and microtubules become exposed at the surface of the cell due to destruction of the reticulated actin cortex [32]. This indicates that dendrite elongation begins at the edge of the cell, and would explain why the shape of the edge of the cell was learned as an informative feature of undifferentiated cells. To verify this hypothesis, additional studies using actin staining are needed to check that such features of the cell edge do actually become neurites.