Feature Fusion Combined With Raman Spectroscopy for Early Diagnosis of Cervical Cancer

Cervical cancer is a serious threat to women's health due to malignant tumours, and early detection can greatly reduce mortality. In this paper, cervical tissue was used as the research object, and Raman spectroscopy analysis of cervical inflammation and precancerous tissues was used to detect cervical cancer. This provides a clinical basis for the use of Raman spectroscopy in analysis of cervical precancerous lesions. In this study, the actual Raman spectrum signal of precancerous cervical tissue was collected, and the PLS and Relief methods were used to extract the signal characteristics of the spectrum. Then, we established and compared KNN and ELM classification models and finally achieved the early diagnosis of cervical cancer. This experiment designed a novel feature fusion method in feature extraction, and we used the first and second derivative features that reflect more peak details of the original spectrum for fusion. The accuracy rate of KNN without feature fusion is 88.17%, and the accuracy rate after fusion is 93.55%. The accuracy rate of ELM without feature fusion is 90.81%, and the accuracy rate after fusion is 93.51%. The results show that the accuracy of feature fusion has been improved to a certain extent, and this method is expected to be used as a new method of spectral data fusion.