Abstract This study presents a novel method for the diagnosis of hepatitis B virus infection using human blood serum Raman spectroscopy combined with a deep learning model. The sera of 499 people infected with the hepatitis B virus and 435 healthy controls were measured in this experiment. The data were subjected to a dimensionality reduction by principal component analysis. Then, the features of multiple scales were preserved and fused by a multiscale fusion convolution operation. The gated recurrent unit network was added to extract time series features and finally output the result of the classification through a softmax layer. A diagnostic model based on a gated recurrent unit and multiscale fusion convolutional neural network was constructed and evaluated by a 10-fold cross-validation method. Compared to existing analysis methods for serum Raman spectroscopy, the proposed model achieved the best performance. The combination of Raman spectroscopy and deep learning models is expected to be applied well in the early screening of hepatitis B and is a promising screening method.

中文翻译：

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拉曼光谱结合门控循环单元和多尺度融合卷积神经网络鉴定乙型肝炎

摘要 本研究提出了一种利用人血清拉曼光谱结合深度学习模型诊断乙型肝炎病毒感染的新方法。本实验测定了499名乙肝病毒感染者和435名健康对照者的血清。通过主成分分析对数据进行降维。然后，通过多尺度融合卷积操作保留和融合多个尺度的特征。添加门控循环单元网络以提取时间序列特征，最终通过 softmax 层输出分类结果。构建了基于门控循环单元和多尺度融合卷积神经网络的诊断模型，并通过 10 倍交叉验证方法进行评估。与现有的血清拉曼光谱分析方法相比，所提出的模型实现了最佳性能。拉曼光谱与深度学习模型的结合有望在乙肝早期筛查中得到很好的应用，是一种很有前景的筛查方法。