Keywords: Tumors (Pre-Treatment), Cancer
Motivation: Accurate classification of multi-type brain tumours through in vivo proton magnetic resonance spectroscopy remains a significant challenge. Conventional machine learning classifiers consider all reliably observed metabolites as features and classify all brain tumours simultaneously, but their performance is limited for rare tumour types.
Goal(s): This abstract presents a novel multi-layer classification model, binary adaptive metabolite selection (BAMS), for better identifying rare tumour types.
Approach: BAMS generalises the problem by considering only one specific brain tumour type and selecting significant biomarkers in each layer iteratively and dynamically.
Results: In comparison to classic models, BAMS showed significantly improved diagnostic performance for rare brain tumour types.
Impact: A brain tumour classification method that can only work on main types and cannot determine rare types is unlikely to be useful for clinicians. This abstract introduces BAMS that can significantly improve diagnostic performance for rare brain tumour types.
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