  The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical
professionals to monitor the progression of a brain tumor. As the interest for
automatizing brain volume MRI analysis increases, it becomes convenient to have
each sequence well identified. However, the unstandardized naming of MRI
sequences makes their identification difficult for automated systems, as well
as makes it difficult for researches to generate or use datasets for machine
learning research. In the face of that, we propose a system for identifying
types of brain MRI sequences based on deep learning. By training a
Convolutional Neural Network (CNN) based on 18-layer ResNet architecture, our
system can classify a volumetric brain MRI as a FLAIR, T1, T1c or T2 sequence,
or whether it does not belong to any of these classes. The network was
evaluated on publicly available datasets comprising both, pre-processed (BraTS
dataset) and non-pre-processed (TCGA-GBM dataset), image types with diverse
acquisition protocols, requiring only a few slices of the volume for training.
Our system can classify among sequence types with an accuracy of 96.81%.
