Predicting arm-chromosomes 1p19q co-deletion and IDH1 mutation in Low Grade Gliomas is determinant in the treatment planning and follow up of the patients. This study aims at proposing a non-invasive method, based on multimodal MR images using convolutional neural networks. The proposed approach consists in several preprocessing steps and an Inception architecture. We present comparative results on a publicly available dataset. The proposed Inception v3 architecture obtain a F1-score of 91.38 ± 5.7% in the test set, when classifying images between 1p19q preserved and codeleted and 82.07% ± 12% when classifying images between with and without IDH1 mutation.