Diagnosis of hepatitis B based on Raman spectroscopy combined with a multiscale convolutional neural network

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 dimensionally 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.