There are several machine learning techniques that are preferred for training algorithms to classify the colonies of stem cells [7–15]. However, unstable classification performances have been demonstrated on colonies that exhibit deformable and changeable morphologies. In other studies using k-nearest neighbor classifier, multiclass quality evaluations of iPSCs based on local features of the colony reportedly had the highest classification accuracy of 62.4% . Other detection methods based on the colony morphology of the stem cells, computed on overlapping blocks of images, revealed moderate accuracy (80%) by means of the linear SVM classifier . A method developed for embryonic stem cell colony segmentation and tracking, using dynamic and morphological features on time data by various machine learning algorithms, revealed both low and high accuracies . However, our current study with deep V-CNN executing batch-based feature vectors of colony morphology has reduced the computational complexity and produced a stable classification performance with an accuracy of 95.5% in discriminating the colonies. In addition, the cross-validated accuracy of the V-CNN classifier (92.4%), as evaluated on the basis of morphological features, was much higher than that of the SVM model (75.2%) in this study. The difference between these results is reasonable, because the proposed system with V-CNN architecture has the advantage of a broader integrated structural complexity to effectively handle a certain degree of variations of features of stem cells and hence produce excellent performance in classifying the colonies, unlike other methods.