Analysis of hepatitis C infection using Raman spectroscopy and proximity based classification in the transformed domain

Biomedical Optics Express
Anabia SohailAsifullah Khan

Abstract

This work presents a diagnostic system for the hepatitis C infection using Raman spectroscopy and proximity based classification. The proposed method exploits transformed Raman spectra using the proximity based machine learning technique and is denoted as RS-PCA-Prox. First, Raman spectral data is baseline corrected by subtracting noise and low intensity background. After this, a feature transformation of Raman spectra is adopted, not only to reduce the feature's dimensionality but also to learn different deviations in Raman shifts. The proposed RS-PCA-Prox shows significant diagnostic power in terms of accuracy, sensitivity, and specificity as 95%, 0.97 and 0.94 in PCA based transformed domain. The comparison of the RS-PCA-Prox with linear and ensemble based classifiers shows that proximity based classification performs better for the discrimination of HCV infected individuals and is able to differentiate the infected individuals from normal ones on the basis of molecular spectral information. Furthermore, it is observed that characteristic spectral changes are due to variation in the intensity of lectin, chitin, lipids, ammonia and viral protein as a consequence of the HCV infection.

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Citations

Oct 1, 2020·Chemical Society Reviews·Nicole M Ralbovsky, Igor K Lednev
Jul 3, 2020·The Journal of Chemical Physics·Loza F TadesseJennifer Dionne
Feb 6, 2020·Biomedical Optics Express·Iris Aguilar-HernándezNancy Ornelas-Soto
Mar 17, 2020·Photodiagnosis and Photodynamic Therapy·Hong ChengXiaoyi Lv
Jan 27, 2021·Biosensors & Bioelectronics·Jijo LukoseSajan D George
Jun 12, 2020·Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy·Xiaoyu CuiYu Zeng

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