Abstract This study presents a method combining serum Raman spectroscopy with a multiscale convolution independent circulation neural network (MSCIR) for hepatitis B virus (HBV) diagnosis. It simplifies the steps of serum Raman spectroscopy data preprocessing and improves the accuracy of hepatitis B diagnosis. Serum samples were obtained from 499 healthy people and 435 HBV patients. First, feature extraction of serum Raman spectroscopy data through principal component analysis (PCA) was performed to reduce the dimensionality of spectral data. Then, the linear discriminant analysis (LDA), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), artificial neural network (ANN) and MSCIR algorithms were employed to establish the discriminant diagnostic models. Their accuracies are 80.77%, 77.69%, 89.23%, 86.92%, 91.53% and 96.15%, respectively. The results show that the MSCIR prediction accuracy is higher than that of the five traditional algorithms, and the fitness is stable. Therefore, the MSCIR algorithm can effectively diagnose hepatitis B patients.

中文翻译：

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基于拉曼光谱结合多尺度卷积神经网络的乙肝诊断

摘要 本研究提出了一种将血清拉曼光谱与多尺度卷积独立循环神经网络 (MSCIR) 相结合的乙型肝炎病毒 (HBV) 诊断方法。简化了血清拉曼光谱数据预处理的步骤，提高了乙肝诊断的准确性。血清样本来自 499 名健康人和 435 名 HBV 患者。首先，通过主成分分析（PCA）对血清拉曼光谱数据进行特征提取，以降低光谱数据的维数。然后，采用线性判别分析（LDA）、k-最近邻（KNN）、支持向量机（SVM）、随机森林（RF）、人工神经网络（ANN）和MSCIR算法建立判别诊断模型。它们的准确率分别为 80.77%、77.69%、89.23%、86.92%、91.53% 和 96.15%，分别。结果表明，MSCIR预测精度高于5种传统算法，适应度稳定。因此，MSCIR算法可以有效诊断乙肝患者。