Rapidly and accurately identifying tumor cells and blood cells is an important part of circulating tumor cell detection. Raman spectroscopy is a molecular vibrational spectroscopy technique that can provide fingerprint information about molecular vibrational and rotational energy levels. Deep learning is an advanced machine learning method that can be used to classify various data accurately. In this paper, the surface-enhanced Raman scattering spectra of blood cells and various tumor cells are measured with the silver film substrate. It is found that there are significant differences in nucleic acid-related characteristic peaks between most tumor cells and blood cells. These spectra are classified by the feature peak ratio method, principal component analysis combined with K-nearest neighbor, and residual network, which is a kind of deep learning algorithm. The results show that the ratio method and principal component analysis combined with the K-nearest neighbor method could only distinguish some tumor cells from blood cells. The residual network can quickly identify various tumor cells and blood cells with an accuracy of 100%, and there is no complex preprocessing for the surface-enhanced Raman scattering spectra. This study shows that the silver film surface-enhanced Raman scattering technology combined with deep learning algorithms can quickly and accurately identify blood cells and tumor cells, indicating an important reference value for the label-free detecting circulating tumor cells.
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.
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