ABSTRACT To detect oral tongue squamous cell carcinoma (OTSCC) using fibre optic Raman spectroscopy, we present a classification model based on convolutional neural networks (CNN) and support vector machines (SVM). 24 samples Raman spectra of OTSCC and para-carcinoma tissues from 12 patients were collected and analysed. In our proposed model, CNN is used as a feature extractor for forming a representative vector. Then the derived features are fed into an SVM classifier, which is used for OTSCC classification. Experimental results demonstrated that the area under the receiver operating characteristic curve was 99.96% and the classification error was zero (sensitivity: 99.54%, specificity: 99.54%). To show the superiority of this model, comparison results with the state-of-the-art methods showed it can obtain a competitive accuracy. These findings may pay a way to apply the proposed model in the fibre optic Raman instruments for intra-operative evaluation of OTSCC resection margins.

中文翻译：

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基于拉曼光谱和卷积神经网络的口腔舌鳞癌分析与分类

摘要 为了使用光纤拉曼光谱检测口腔舌鳞状细胞癌 (OTSCC)，我们提出了一种基于卷积神经网络 (CNN) 和支持向量机 (SVM) 的分类模型。收集并分析了来自 12 名患者的 OTSCC 和癌旁组织的 24 个样本拉曼光谱。在我们提出的模型中，CNN 用作特征提取器以形成代表向量。然后将导出的特征输入到 SVM 分类器中，用于 OTSCC 分类。实验结果表明，受试者工作特征曲线下面积为99.96%，分类误差为零（敏感性：99.54%，特异性：99.54%）。为了显示该模型的优越性，与最先进方法的比较结果表明它可以获得具有竞争力的准确性。