Recently, several supervised machine learning approaches have also been developed and their significance in distinguishing stem cell colonies confirmed [12–15]. The approaches designed for the selection of colonies, based on k-nearest neighbor searching with  and without error correction output codes , ensemble support vector machine (SVM) , and random forest methods , acquired local features from the patches of original images. In addition, the development of the learning set for discrimination of cells in the colony has to be done manually. However, a high degree of reliability and cost-effectiveness is very important in clinical applications. Recently, many researchers have focused on implementing the convolutional neural network (CNN) for various medical imaging modalities, and its high reliability and validity for object segmentation and detection applications have been revealed [16, 17]. In addition, CNNs have been successfully applied to microscopic cell imaging data, with robust decisions on ambiguous cell classifications found, making the process suitable for clinical applications [18–20]. A deep CNN method for identifying mitosis in the cell nucleus reportedly had higher satisfactory performance than estimation with conventional methods . A deep multiple instance learning-based CNN approach effectively segmented mammalian and yeast microscopy images with remarkable accuracy . However, the above-mentioned CNN approaches on biomedical imaging data differ from our study, as they have adopted deep neural networks for image segmentation tasks. In this study, our target was to train a CNN model to classify colonies on the basis of obtained features of the segmented colony. Most of the aforementioned studies tested colony morphology for estimating colony categories. However, apart from certain quantitative morphological features, textural features are the most important, as they describe the spatial intensity variations of the colony image. Furthermore, textural features are closely connected with cellular characteristics . Hence, this study considered both colony morphological and textural features for the evaluation of iPSCs.