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
1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 2Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom
Wavelet oversampling showed significantly improved classification performance of childhood brain tumours through metabolite profiles from 1H-MRS.
Illustration showing the procedure of generating oversampled proton magnetic resonance spectroscopy (1H-MRS) from the raw 1H-MRS by using Wavelet OverSampling (WvOS). Abbreviations: SNR, signal-to-noise ratios; ψ, wavelet basis.
Boxplots showing the balanced classification accuracy for the 1.5T cohort of childhood brain tumours derived through linear discriminant analysis (A, C) or supper vector machine (B, D) with leave-one-out (A-B) or six-fold (C-D) cross validation.
