Frequency selection for the diagnostic characterization of human brain tumours
This work addresses the complex clinical task of brain tumor diagnosis for medical professionals, but it appears incremental as it builds on existing pattern recognition techniques without claiming major breakthroughs.
The paper tackled the problem of diagnosing brain tumors using high-dimensional magnetic resonance spectroscopy data by applying a spectral frequency selection procedure combined with nonlinear classification, but no concrete results or numbers were reported.
The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable. In this brief paper, an international database of brain tumours is analyzed resorting to an ad hoc spectral frequency selection procedure combined with nonlinear classification.