Several recent studies have demonstrated the highly significant contributions of CNN toward microscopic cell image segmentation compared with other conventional methods [19, 37]. A deep CNN for bacterial colony segmentation demonstrated its superior performance over the SVM method, similar to our study . Bacterial colony enumeration using CNN obtained improved precision and recall values that were almost similar to those obtained in the current study . However, these aforementioned studies used CNN for the segmentation of image objects, whereas our current study incorporated CNN for the classification of feature vectors of images, and hence the results are strictly speaking not directly comparable. Most of the previous studies considered colony morphology to be the most important criterion for estimating colony categories [5, 8, 38]. In the study by Zahedi et al. , area was the best individual feature for colony discrimination. Contrary to that study, another report found the shape-based solidity feature to have the strongest ability in classifying stem cells, similar to what was found in our current study . However, as the clinical target, feature descriptors, and classifier models of those studies were different from ours, the studies might not be directly comparable. The limitation of our study was the small number of training data used to build the classifier model. Further studies with a much larger number of training data, with various objectives (10×, 20×, and 40×) of iPSC colony images as well as live cell imaging evaluation, should be used to evaluate the performance of the proposed V-CNN model. Furthermore, the present model analyzed gray-level co-occurrence matrix-based texture features for quality determination. In future, the usefulness of other textural features, such as discrete wavelet and geometric moment based analysis, could be considered to enhance and generalize the proposed model.