Multiclass Imbalance Learning: Improving Classification of Pediatric Brain Tumors from Magnetic Resonance Spectroscopy

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Classification of pediatric brain tumors from 1H-magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces difficulties in classifying rare tumor groups. This study assessed different imbalanced multiclass learning techniques and compared the use of complete spectra and quantified metabolite profiles for classification of three main childhood brain tumor types.


Single-voxel, Short echo time MRS data were collected from 90 patients with pilocytic astrocytoma (n = 42), medulloblastoma (n = 38), or ependymoma (n = 10). Both spectra and metabolite profiles were used to develop the learning algorithms. The borderline synthetic minority oversampling technique and AdaboostM1 were used to correct for the skewed distribution. Classifiers were trained using five different pattern recognition algorithms.


Use of imbalanced learning techniques improved the balanced accuracy rate (BAR) of all classification methods (average BAR over all classification methods for spectra: oversampled data = 0.81, original = 0.63, P < 0.001; metabolite concentration: oversampled-data = 0.91, original = 0.75, P < 0.0001). Performance of all classifiers in discriminating ependymomas increased when oversampled data were used compared with original data for both complete spectra (F-measure P < 0.01) and metabolite profile (F-measure P < 0.001).


Imbalanced learning techniques improve the classification accuracy of childhood brain tumors from MRS where group sizes differ and facilitate the inclusion of rarer tumor types into clinical decision support systems.

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