Brain tumors have been one of the most common life-threatening diseases for all mankind. There have beenhuge efforts dedicated to the development of medical imaging techniques and radiomics to diagnose tumor patients quicklyand e?iciently. One of the main aims is to ensure that preoperative overall survival time (OS) prediction is accurate.Recently, deep learning (DL) algorithms, and particularly convolutional neural networks (CNNs) achieved promisingperformances in almost all computer vision fields. CNNs demand large training datasets and high computational costs.However, curating large annotated medical datasets are difficult and resource-intensive. The performances of singlelearners are also unsatisfactory for small datasets. Thus, this study was conducted to improve the performance of CNN models on small volumetric datasets through developing a DL-based ensemble method for OS classification of brain tumorpatients using multimodal magnetic resonance images (MRI). First, we proposed multiview CNNs: OS classifiers basedon representing the 3D MRI data as a set of 2D slices along all three planes (axial, sagittal, and coronal) and processthem using 2D CNNs. Subsequently, the predicted probabilities by the multiview CNN models were fused using standardmachine learning algorithms. The proposed approach was experimentally evaluated on 163 patients obtained from theBraTS?17 training dataset. Our best model achieved an AUC and accuracy values of 0.93 and 92.9%, respectively, onclassifying patients with brain tumors into two OS groups, outperforming current state-of-the-art results. In addition,the FLAIR MRI modality yielded the best classification accuracy compared to other MRI modalities. Similarly, axialprojections had the best classification performance compared to coronal and sagittal projections. Our findings mayprovide valuable insights for physicians in advancing treatment planning via noninvasive and accurate prediction ofsurvival using only MRIs at the time of diagnosis.