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Analysis and Classification of Hepatitis Infections Using Raman Spectroscopy and Multi-Scale Convolutional Neural Networks
Abstract
Hepatitis infections represent a major health concern worldwide. Numerous computer-aided approaches have been devised for the early detection of hepatitis. In this study, we propose a method for the analysis and classification of cases of hepatitis-B virus (HBV), hepatitis-C virus (HCV), and healthy subjects using Raman spectroscopy and a multi-scale convolutional neural network (MSCNN). In particular, serum samples of HBV-infected patients (435 cases), HCV-infected patients (374 cases), and healthy persons (499 cases) are analyzed via Raman spectroscopy. The differences between Raman peaks in the measured serum spectra indicate specific biomolecular differences among the three classes. The dimensionality of the spectral data is reduced through principal component analysis. Subsequently, features are extracted, and then feature normalization is applied. Next, the extracted features are used to train different classifiers, namely MSCNN, a single-scale convolutional neural network, and other traditional classifiers. Among these classifiers, the MSCNN model achieved the best outcomes with a precision of 98.89%, sensitivity of 97.44%, specificity of94.54%, and accuracy of94.92%. Overall, the results demonstrate that Raman spectral analysis and MSCNN can be effectively utilized for rapid screening of hepatitis B and C cases.
Supporting agencies: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61765014); Reserve Talents Project of National High-level Personnel of Special Support Program (QN2016YX0324); Urumqi Science and Technology Project (No. P161310002 and Y161010025); and Reserve Talents Project of National High-level Personnel of Special Support Program (Xinjiang [2014]22)
About the Authors
Y. Zhao
Xinjiang University
China
China
Key Laboratory of Software Engineering Technology
Urumqi 830000
Sh. Tian
Xinjiang University
China
China
Key Laboratory of Software Engineering Technology
L. Yu
College of Software Engineering at Xin Jiang University
China
China
Urumqi 830000
Zh. Zhang
The First Affiliated Hospital of Xinjiang Medical University
China
China
Urumqi 830000
W. Zhang
Xinjiang University
China
China
Key Laboratory of Software Engineering Technology
Urumqi 830000
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Review
For citations:
Zhao Y., Tian Sh., Yu L., Zhang Zh., Zhang W. Analysis and Classification of Hepatitis Infections Using Raman Spectroscopy and Multi-Scale Convolutional Neural Networks. Zhurnal Prikladnoii Spektroskopii. 2021;88(2):338(1)-338(11).
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