Detection of Basal Cell Carcinoma by Automatic Classification of Confocal Raman Spectra
Detection of Basal Cell Carcinoma by Automatic Classification of Confocal Raman Spectra
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通过共焦拉曼光谱自动分类检测基底细胞癌
DOI：
10.1007/11816102_44
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发表时间：
2006
影响因子：
2.1
通讯作者：
J. Choo
中科院分区：
文献类型：
--
作者：
Seong;A. Park;J. Y. Kim;S. Na;Yonggwan Won;J. Choo
拉曼光谱在提供皮肤癌的无创皮肤科诊断方面具有很强的潜力。在这项研究中，我们研究了不同的共聚焦拉曼光谱分类方法检测基底细胞癌（BCC），这是最常见的皮肤癌之一。这些方法包括最大后验概率（MAP）、概率神经网络（PNN）、k近邻（KNN）、多层感知器网络（MLP）和支持向量机（SVM）。分类框架包括拉曼光谱预处理、特征提取和分类。在预处理步骤中，采用简单的半汉宁方法获得鲁棒特征。在MLP和SVM的情况下，共聚焦拉曼光谱对216个光谱的分类结果的真分类率约为97%，证明了共聚焦拉曼光谱对BCC检测的有效性。此外，对分类有重要意义的光谱区域进行了灵敏度分析。
Raman spectroscopy has strong potential for providing noninvasive dermatological diagnosis of skin cancer. In this study, we investigated various classification methods with confocal Raman spectra for the detection of basal cell carcinoma (BCC), which is one of the most common skin cancer. The methods include maximum a posteriori (MAP) probability, probabilistic neural networks (PNN), k-nearest neighbor (KNN), multilayer perceptron networks (MLP), and support vector machine (SVM). The classification framework consists of preprocessing of Raman spectra, feature extraction, and classification. In the preprocessing step, a simple half Hanning method is adopted to obtain robust features. Classification results involving 216 spectra gave about 97% true classification rate in case of MLP and SVM, which is an evident proof of the effectiveness of confocal Raman spectra for BCC detection. In addition to it, spectral regions, which are important for classification, are examined by sensitivity analysis.