Due to the high complexity of geometry-deterministic wireless channel modeling and the difficulty in its implementation, geometry-based stochastic channel modeling (GBSM) approaches have been used to evaluate system performance of wireless communications. This paper introduces a new method to model a GBSM by training a generative neural network using images formed by channel parameters. Toward this end, we process the data of channel parameters in the form of images and train the generative neural networks where the convolutional layers are mainly employed. Through a case study, we confirm that the use of channel images facilitates the training of the generative model and ensures that the model learns the correlations among multipath components. We show that the outputs of the generative model faithfully represent the distributions of the original data. Furthermore, to corroborate applicability of the trained model, we run simple system-level simulations and show the results from the trained model and ray-tracing data are well matched. Therefore, we argue that the proposed model trained by channel images will ease the burden of GBSM implementations with general wireless conditions and capture the statistical joint distributions of the original channel data.