Colonoscopy is regarded as the gold standard in colorectal tumor diagnosis, but it is costly and time-consuming. Raman spectroscopy has shown promise for differentiating cancerous from non-cancerous tissue and is expected to be a new tool for oncological diagnosis. However, traditional Raman spectroscopy analysis requires tedious preprocessing, and the classification accuracy needs to be improved. In this work, a novel Raman spectral qualitative classification method based on convolutional neural network (CNN) is proposed for the identification of three different colon tissue samples, including adenomatous polyp, adenocarcinoma and normal tissues. Experimental results show that this CNN model has superior feature extraction ability. For the spectral data of new individuals, the trained CNN model presents much better classification performance than traditional machine learning methods, such as the k-nearest neighbor, random forest, and support vector machine. Raman spectroscopy combined with CNN can be used as an effective auxiliary tool for the early diagnosis of colon cancer.

结肠镜检查被认为是结直肠肿瘤诊断的金标准，但成本高、耗时长。拉曼光谱已显示出区分癌组织和非癌组织的前景，有望成为肿瘤学诊断的新工具。然而，传统的拉曼光谱分析需要繁琐的预处理，分类精度有待提高。在这项工作中，提出了一种基于卷积神经网络 (CNN) 的新型拉曼光谱定性分类方法，用于识别三种不同的结肠组织样本，包括腺瘤性息肉、腺癌和正常组织。实验结果表明，该CNN模型具有优越的特征提取能力。对于新个体的光谱数据，经过训练的 CNN 模型呈现出比传统机器学习方法（例如 k 最近邻、随机森林和支持向量机）更好的分类性能。拉曼光谱结合CNN可作为结肠癌早期诊断的有效辅助工具。