CNNs are a branch of neural networks that have been implemented successfully in image recognition and classification [16–21]. Although the CNN has been applied for various medical imaging segmentations, it has not been used previously for the input of feature vector-based classifications for colony quality. The selected features of colony morphology and textures obtained from the segmented colony were entered into the V-CNN model in order for the classification task to identify the colony quality. However, input feature vectors cannot be entered directly into the typical CNN. Hence, we added a transfer function from the feature vectors to the virtual image at the front of the CNN model organization. In addition, the parameters of the mapping function were trained to obtain an adequate transfer function for a target classification task of the CNN framework. Hereafter, we briefly explain the mathematical framework of V-CNN and the process of training to implement the classification task. The V-CNN architecture used in this study is arranged by stacking a set of convolutional, transfer function, and pooling layers in an alternate way, as shown in Fig 5. The main work of the convolutional layer is to estimate local conjunctions of features from the input feature vectors and to map their occurrence to a feature map. As a result of convolution in neuronal networks, the feature vectors are partitioned into perceptrons, generating local flexible fields and finally trampling the perceptron into feature maps of size n1 × n2. In each layer, there is a bank of n filters that detect features at every location of the input. The output Ya(q) of layer q consists of n(q) feature maps of size n1(q)×n2(q). The ath feature map, indicated as Ya(q), is computed as Ya(q)=Ga(q)+∑b=1n(q−1)Va,b(q)*Yb(q−1)(8) where Ga(q) is a bias matrix and Va,b(q) is the filter of size 2t1(q)+1×2t2(q)+1, connecting the bth feature map in layer (q − 1) with the ath feature map in the layer. The weights of these filters and their values are altered throughout the training to reduce the classification error on any training data. The next operation is to apply the transfer function to produce a set of feature maps. This helps the classifier to build nonlinear decision boundaries. The selection of an activation function has a strong influence on the computational costs of both training and validation performances. Hence, in this study, we chose the rectified linear unit (ReLU) as a transfer function, which is many times faster than the other activation functions. It is defined as Ya(q)=max(0,Ya(q−1))(9)