Detection and quantification of White Matter Hyperintensities (WMH) on T2-FLAIR images can provide valuable information to assess neurological disease progression. We propose a fully automated stacked generalization ensemble of three orthogonal 3D Convolutional Neural Networks (CNNs), StackGen-Net, to detect WMH on 3D FLAIR images. Each orthogonal CNN predicts WMH on axial, sagittal, and coronal orientations. The posteriors are then combined using a Meta CNN. StackGen-Net outperforms individual CNNs in the ensemble, their ensemble combination, as well as some state-of-the-art deep learning-based models. StackGen-Net can reliably detect and quantify WMH in clinically feasible times, with performance comparable to human inter-observer variability.