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703 results on '"Lv, Xiaoyi"'

79. Multiscale Convolutional Neural Network of Raman Spectra of Human Serum for Hepatitis B Disease Diagnosis

Author

Cheng, Junlong, Yu, Long, Tian, Shengwei, Lv, Xiaoyi, and Zhang, Zhaoxia

Subjects
Cable News Network, Cable television broadcasting industry -- Analysis -- Health aspects, Raman spectroscopy -- Health aspects -- Analysis, Natural language interfaces -- Health aspects -- Analysis, Computational linguistics -- Health aspects -- Analysis, Language processing -- Health aspects -- Analysis, Hepatitis B -- Analysis -- Health aspects, Machine learning -- Analysis -- Health aspects, Neural networks -- Analysis -- Health aspects, Neural network, Chemistry, Engineering and manufacturing industries, Physics, Science and technology
Abstract

In this study, we proposed a multiscale convolutional neural network (MsCNN) that can screen the Raman spectra of the hepatitis B (HB) serum rapidly without baseline correction. First, the Raman spectra were measured in the serums of 435 patients diagnosed with a HB virus (HBV) infection and 499 patients with non-HBV infections. The analysis showed that the Raman spectra of the serums were significantly different in the range of 400-3000 [cm.sup.-1] between HB patients and non-HB patients. Then, the MsCNN model was used to extract the nonlinear features from coarse to fine in the Raman spectrum. Finally, extracted fine-grained features were placed into the fully connected layer for classification. The results demonstrated that the accuracy, sensitivity, and specificity of the MsCNN model are 97.86%, 98.94%, and 96.79%, respectively, without baseline correction. Compared to the traditional machine learning method, the model achieved the highest classification accuracy on the HB data set. Therefore, multiscale convolutional neural network provides an effective technical means for Raman spectroscopy of the HBV serum., Hepatitis B (HB) is caused by infection with the HB virus (HBV), a kind of infectious disease dominated by liver inflammatory lesions. There are approximately two billion people infected with [...]

Published
2022

96. MURDA: Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains for Medical Diagnosis Assistance

Author

Liu, Yang, Chen, Chen, Zuo, Enguang, Yan, Ziwei, Chang, Chenjie, Cheng, Zhiyuan, Lv, Xiaoyi, and Chen, Cheng

Abstract

Artificial intelligence combined with Raman spectroscopy for disease diagnosis is on the rise. However, these methods require a large amount of annotated spectral data for modeling to achieve high diagnostic accuracy. Annotating labels consumes significant medical resources and time. To reduce dependence on labeled medical data resources, we propose a method called Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains (MURDA). It transfers knowledge learned from source domain data sets of different diseases to an unlabeled target domain data set. Compared to knowledge transfer from a single source domain, knowledge from multiple disease source domains provides more generalized knowledge. Considering the diversity of autoimmune diseases and the limited sample size, we apply MURDA to assist in the medical diagnosis of autoimmune diseases. Additionally, we propose a Double-Branch Multiscale Convolutional Self-Attention (DMCS) feature extractor that is more suitable for spectral data feature extraction. On three sets of serum Raman spectroscopy data sets for autoimmune diseases, the multisource domain adaptation diagnostic accuracy of MURDA was superior to traditional single source and multisource models, with accuracy rates of 73.6%, 83.4%, and 82.9%, respectively. Compared with pure source tasks without domain adaptation, it improved by 15.1%, 36%, and 21.6%, respectively. This demonstrates the effectiveness of Raman spectroscopy combined with MURDA in diagnosing autoimmune diseases. We investigated the important decision dependency peaks in migration tasks, providing assistance for future research on artificial intelligence combined with Raman spectroscopy for diagnosing autoimmune diseases. Furthermore, to validate the effectiveness and generalization performance of MURDA, we conducted experiments on the publicly available RRUFF data set, exploring the application of multisource unsupervised domain adaptation in more Raman spectroscopy scenarios.

Published
2024
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