Abstract #2658

Imbalanced learning techniques for improved classification of paediatric brain tumours from magnetic resonance spectroscopy

Niloufar Zarinabad^1,2, Christopher Bennett^1,2, Simrandip Gill^1,2, Martin P Wilson¹, Nigel P Davies^1,2,3, and Andrew Peet^1,2

¹Institute of Cancer and Geonomic Sciences, University of Birmingham, Birmingham, United Kingdom, ²Birmingham Children’s Hospital NHS foundation trust, Birmingham, United Kingdom, ³Department of Medical Physics,University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom

Classification of paediatric brain tumours from Magnetic-Resonance-Spectroscopy has many desirable characteristics. However the imbalanced nature of the data introduces difficulties in uncovering regularities within the small rare tumour type group and attempts to train learning algorithms without correcting the skewed distribution may be premature. By fusing oversampling and classification techniques together, an improved classification performance across different classes with a good discrimination for minority class can be achieved. The choice of learning algorithm, use of oversampling-technique and classifier input (complete spectra versus metabolite-concentration) depends on the data distribution, required accuracy in discriminating specific groups and degree of post-processing complexity.

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