The performance of the V-CNN model in determining the quality of iPSC colonies on the basis of various feature sets is presented in Table 2. The accuracy of the morphological features in assessing the colony quality was slightly higher than that of the textural features. In addition, with the V-CNN model, the accuracy of the morphological (95.5%), textural (91.0%), and combined (93.2%) features in determining the quality of colonies was higher than that of those features (86.7%, 83.3%, and 83.4%, respectively) used in the SVM classifier. Furthermore, to validate the performance of the proposed model, precision, recall, and F-measures were used . Precision demonstrates the number of positive predictions divided by the total number of positive class values predicted, and is defined as Precision=∑i=1c(TPi)∑i=1c(TPi+FPi)(12)