## 4 Discussion
In the present study, photomicrograph imaging of single SH-SYSY cells comprised the learning data for differentiation classification of cells. This study has indicated that the inhomogeneous differentiation status of each cell can be distinguished by its CNN model, through recognising critical distinct morphological features. When cellular differentiation is induced in vitro, this is usually carried out in densely cultured cells. The work of [PERSON] et al. demonstrated that differentiation status could be accurately classified in inhomogeneously differentiated densely cultured cells [16]. The present study focused primarily on the cellular morphological features and their relationship to differentiation status. Further work is needed to clarify whether the CNN model developed in thepresent study is able to accurately classify differentiation status in inhomogeneously differentiated densely cultured cells. Because our model is a non-invasive means of distinguishing between differentiated and undifferentiated SH-SY5Y cells, we examined the differentiation of SH-SY5Y cells over time (Figure 6). We observed that the number of differentiated cells began to increase at 10 days after the induction of differentiation. A previous study demonstrated that 90% of SH-SY5Y cells stopped proliferating at five or three days after the addition of all-trans retinoic acid or brain-derived neurotrophic factor, respectively, with most of the cells arrested in the G1 phase; it was speculated that these arrested cells gradually began to differentiate from that time point on [18]. We hypothesize that the increase in differentiation we observed at 10 days after the induction of differentiation could be related to this phenomenon. The gradual differentiation of SH-SY5Y cells after day 10 was also confirmed by the distributions of the feature maps output from the fully connected layer in the two-dimensional space using t-SNE; the distributions of the feature maps for days two to eight (Figure 7b-f) showed a greater ratio of undifferentiated cells, whereas those for day 10 onwards showed a greater ratio of differentiated cells (Figure 7g-m). These results support that our model could correctly identify the start of and gradual progression of differentiation in SH-SY5Y cells. Our data also showed that the differentiation ratio fluctuated over time depending on the subculture (Figure 6). This implies that differentiation proceeds at different rates in different subcultures, and so generalizations about the rate of differentiation of SH-SY5Y cells cannot be made. A number of effective network architectures exist for a learning dataset of photomicrographs in a time-series in order to acquire a differentiation ratio over time (Figure 6). These include long short term memory (LSTM) and recurrent neural network (RNN). Previous research has demonstrated that a learning model consisting of CNN and LSTM can detect cells in mitosis from phase-contrast photomicrographs in time-series [33]. Another study created a learning model using CNN and RNN that was highly accurate in distinguishing differentiated hematopoietic stem cells from progenitor cells using time-series bright-field photomicrographs [34]. These learning models were known to be effective for the analysis of time-series data, however they were found to be too complex to determine the most significant morphological features that informed differentiation classification. The key purpose of the current study was to identify the critical cellular morphological features that would facilitate highly accurate classification of differentiated versus undifferentiated cells. Therefore, learning models such as LSTM and RNN were not adopted in this study. A need to formalize the methodology of  
Figure 8: Comparison of feature maps obtained from our convolutional neural network model and from the model developed by [PERSON] et al. [16] (**a**) Input phase-contrast photomicrograph. (**b**) Feature maps obtained from the model of [PERSON] et al.