In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the strengths of both non-generative classifiers and generative modeling. Its purpose is to streamline the creation of new classifiers by embedding existing compatible classifiers in a generative network architecture. The demonstration of this process and evaluation of its effects is performed using a 3D convolutional classifier and its generative equivalent - a conditional generative adversarial network classifier. The results show that the generative model achieves greater classification performance, gaining a relative classification accuracy improvement of 7.43%. Improvement of accuracy is also present when compared to a plain convolutional classifier trained on a dataset augmented with examples produced by a trained generator. This suggests there is a desirable knowledge sharing within the hybrid discriminator-classifier network.