通过频段筛选与非线性分类，提升脑瘤诊断准确性
Frequency selection for the diagnostic characterization of human brain tumours
- 基于专用频段选择方法提取关键代谢信号
- 在国际脑瘤数据库上实现高精度分类
- 适合医学影像分析与精准诊断研究者参考
脑瘤诊断是一项高度敏感且复杂的临床任务，需依赖非侵入性技术获取信息。磁共振成像或波谱学是常用手段，其中波谱学能提供丰富的肿瘤组织代谢信息，但其高维特性促使采用模式识别技术。本文针对一个国际脑瘤数据库，结合专有的光谱频段选择方法与非线性分类器进行分析，有效提升了诊断特征的可区分性。
原文摘要 · Abstract (English)
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.
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