Serum Raman spectroscopy combined with convolutional neural network for label‐free detection of echinococcosis

Early disease screening is of great significance to its prevention and treatment, and serum analysis is one of the important methods for early disease screening. In this paper, a portable spectrometer was used to collect serum Raman spectra of echinococcosis patients and healthy volunteers, and a one‐dimensional convolutional neural network (CNN) was used to classify the two types of spectra. The average spectrum shows that the phenylalanine and carotene content in the serum of patients with echinococcosis is less than that in healthy volunteers, whereas the content of amino acids such as proline and tyrosine is higher. We built a CNN model to identify two types of spectra. First, the Kennard–Stone algorithm was used to divide the two types of spectra into a training set and a test set. Afterwards, the optimal hyperparameters of the model were determined by fivefold cross‐validation on the training set. Finally, the test set was used to evaluate the final effect of the model. The accuracy, sensitivity, and specificity of the test set on the model are 94.90%, 90.57%, and 97.11%, respectively. The receiver operating characteristic curve analysis further confirmed the excellent performance of the CNN in the classification of serum Raman spectra. The above results show that serum Raman spectroscopy combined with a one‐dimensional CNN algorithm has great potential in the early diagnosis of echinococcosis. We used a 532‐nm laser source portable spectrometer to collect serum Raman spectra of healthy people and echinococcosis patients. A convolutional neural network was used to distinguish the two types of spectra. The results show that the convolutional neural network has great potential in the screening of echinococcosis.