Patients with 1p/19q co-deleted low grade glioma (LGGs) have better prognosis and react better to certain treatments than patients with intact 1p/19q LGG. Currently, information about the 1p/19q co-deletion status is obtained by means of an invasive procedure called biopsy. As an alternative, non-invasive techniques to extract this information from medical images are being studied. Recent research suggests that local binary patterns (LBPs), a textural image descriptor, are an important feature which can predict the 1p/19q co-deletion from MRI scans. In this project we report the effect of including LBP information in a convolutional neural network (CNN) to predict the 1p/19q co-deletion status in patients suffering from a presumed LGG using pre-operative MRI scans. A combination of convolutional filters was designed and included in the CNN, resulting into local binary convolutional neural networks (LBCNNs). Three LBP descriptors, each of them representing a different textural scale, were studied, as well as the combination of the three. A default CNN without LBPs was also studied. To validate the designed filters and to study more sophisticated LBPs images like the uniform LBPs, pre-computed LBP images were directly input to the CNN. An in-house multi-institution MRI dataset consisting of 284 patients who had undergone a biopsy or resection before the treatment, and with available pre-operative T1-weighted post contrast and T2-weighted scans was used to train the different network architectures. An independent dataset consisting of 129 patients was used to validate the results. The performance of the LBCNNs was compared to the performance of the CNN. The performance of the CNN and LBCNNs was similar, reporting an area under the receiver operating characteristic curve (AUC) ranging from 0.816 to 0.872 for the different architectures. These findings suggest that the CNN can extract information relative to LBPs by itself. In addition, pre-computed uniform LBPs report similar metrics (AUC: 0.819), suggesting that they do not add new information.