In order to analyze the classification performance, the selected features were inserted into the V-CNN model to distinguish the healthy and unhealthy colonies of iPSCs. Since feature vectors cannot be entered directly into the CNN network, we added a transfer function from the feature vectors (11) to the virtual image at the front of the CNN organization. We found that (28 × 28) size of the virtual image was optimum by experiment to produce better results in this study. This was then entered through a stack of two-dimensional convolutional layers of 32 filters with convolution kernel sizes of 3 × 3 throughout the operation of the network. The training process of the model was performed using a labeled dataset of input feature vectors. The testing size of 0.33 and random_state with seed value of 9 was observed to produce best performance of the proposed model. The total dataset was then divided into 60 for training and 30 for testing the model. The V-CNN model had a higher capacity in classifying the quality of colonies, as indicated by its high accuracy and low loss values (Fig 9). The estimated loss values for the morphological, textural, and combined features were low (0.209, 0.285, and 0.202, respectively), implying the behavior of the model after 20 iterations of optimization. In addition, the performance of the V-CNN model was further compared with that of the SVM classifier, using the radial basic function kernel. Quadratic programming was applied to optimize the parameters of the SVM model and the program used in the experiments was implemented using Scikit-learn toolkit . The hyper-parameters γ that control the capacity of the kernel and C, the regularization parameter [34, 35] were determined by using cross-validation. The combination of parameters were observed to produce better results at γ = 2 and C = 1.