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Abstract #0937

Wavelet Oversampling for Imbalance Childhood Brain Tumour Classification

Dadi Zhao1,2, James T. Grist1,2, Heather E.L. Rose1,2, Yu Sun1,2, and Andrew C. Peet1,2
1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 2Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom

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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