Pattern recognition and classification in high-resolution magnetic resonance spectra

Abstract

We show the impacts of various signal preprocessing techniques - dimensionality reduction and transformations - for high-resolution NMR spectra on the classification accuracy of different breast cancer tissue. Our results show that some preprocessing algorithms that are widely used nowadays will not reduce the data dimensionality in an information-preserving way: the classification accuracy drops. Besides showing the most successful preprocessing steps, we can report excellent results on a challenging classification problem.