  Magnetic Resonance Spectroscopy (MRS) provides valuable information to help
with the identification and understanding of brain tumors, yet MRS is not a
widely available medical imaging modality. Aiming to counter this issue, this
research draws on the advancements in machine learning techniques in other
fields for the generation of artificial data. The generated methods were tested
through the evaluation of their output against that of a real-world labelled
MRS brain tumor data-set. Furthermore the resultant output from the generative
techniques were each used to train separate traditional classifiers which were
tested on a subset of the real MRS brain tumor dataset. The results suggest
that there exist methods capable of producing accurate, ground truth based MRS
voxels. These findings indicate that through generative techniques, large
datasets can be made available for training deep, learning models for the use
in brain tumor diagnosis.
