The precision, recall, and F-measure values generated in the current study with the V-CNN model were high that indicated the fewer numbers of false positives and false negatives than those generated by the competing SVM classifier (Table 2). Furthermore, the reliability and generalization of the proposed V-CNN model were investigated using a five-fold cross-validation method [30, 31], which functions by partitioning the datasets into k parts (k = 5). The partitioned data are represented as a fold. The method was trained on k–1 folds with one held back, and tested on the held back fold. This was continued five times separately, applying different members of the training and testing data that possess compositions different from those of the other experiment. The mean value of these five different compositions of classification performance was evaluated and considered as the overall accuracy of the model. The experimental results of the five-fold cross-validation of the performance of the V-CNN and SVM classifiers in determining the quality of iPSC colonies on the basis of various feature sets are presented in Table 3. The overall accuracy results using five-fold cross-validation for the V-CNN were much higher than those of the SVM, which was more than 10%. Similarly, five-fold cross-validation of the performance of the two tested models in evaluating the precision, recall, and F-measures produced high values in the range of 85–89% for V-CNN and very low values in the range of 68–83% for SVM, indicating the robustness and effectiveness of the proposed V-CNN approach in determining the quality of colonies.