In conclusion, our newly proposed framework of interfacing image processing methods with the V-CNN model produced encouraging results in determining the iPSC colony quality. Although the CNN has been applied before for microscopic cell image segmentation, this is the first implementation for input feature vector classification of colony quality. The suitability of the V-CNN model for addressing the classification task has been successfully shown, revealing it to have higher classification accuracy than that of the competitive SVM classifier. The proposed V-CNN-based colony identification system has been experimentally tested and cross-validated to be the most optimal model. Additionally, the proposed approach does not require much computational resources, and reduces on architectural and computational complexities, and thus it can be implemented as a valuable tracking technique in a real-time classification system. Overall, our experimental results indicated that the proposed deep V-CNN approach can allow the accurate, rapid detection of colony quality, outperforms the state-of-the-arts, and thus it can be a promising decision support model for clinical applications.