A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum.

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Abstract

This research innovatively combines FT-IR, Raman spectroscopy and their first-derivative spectroscopy to develop a rapid diagnosis method for renal cell carcinoma (RCC). After measuring the Raman spectra and FT-IR spectra of 45 cases of control subjects and 28 cases of RCC, the first derivative of the infrared spectra and the Raman spectra were calculated respectively. Principal component analysis (PCA) was used to extract the features of the infrared spectra, first-derivative infrared spectra, Raman spectra and first-derivative Raman spectra. Then the four feature matrices were merged as fused spectral feature matrices. The fused matrices were used as the input of AlexNet and MCNN. The fused spectral feature matrices were used as the input of AlexNet and MCNN. The adjusted AlexNet model performed better, and the classification accuracy of the fused spectral data is 93%. Compared with the classification results of infrared spectra (74%), Raman spectra (75%) and the fusion of infrared and Raman spectra (79%) combined with the adjusted AlexNet model, the classification result of the fusion of infrared spectra, Raman spectra and their first-derivative was significantly improved. The experimental results show that infrared spectroscopy, Raman spectroscopy and their first-derivative fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC.Copyright © 2021 Elsevier B.V. All rights reserved.