Classifying imbalance childhood brain tumours through 1H-MRS metabolite profiles remains a challenging problem. We presented an alternative oversampling method, wavelet oversampling (WvOS). Different from the classic Synthetic Minority Oversampling TEchnique that oversamples the metabolite profiles, WvOS used the wavelet processed 1H-MRS as the oversampled 1H-MRS, followed by quantification and classification. As the result, WvOS can provide dramatically better classification performance than non-oversampled or classic oversampled metabolite profiles. An optimal balanced classification accuracy is achieved as 96% and 72% from 84% and 52% for the 1.5T and 3T cohorts of childhood brain tumours, respectively.
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