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Xiong C, Zhong Q, Yan D, Zhang B, Yao Y, Qian W, Zheng C, Mei X, Zhu S. Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders. BIOMEDICAL OPTICS EXPRESS 2024; 15:3523-3540. [PMID: 38867772 PMCID: PMC11166416 DOI: 10.1364/boe.514196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 06/14/2024]
Abstract
Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.
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Affiliation(s)
- Changchun Xiong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Qingshan Zhong
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China
| | - Denghui Yan
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
| | - Baihua Zhang
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Yudong Yao
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Wei Qian
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
| | - Chengying Zheng
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Xi Mei
- Department of Psychiatry, Ningbo Kangning Hospital and Affiliated Mental Health Centre, Ningbo Key Laboratory for Physical Diagnosis and Treatment of Mental and Psychological Disorders, Ningbo University, Ningbo 315211, China
| | - Shanshan Zhu
- Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo 315211, China
- Health Science Center, Ningbo University, Ningbo 315211, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology , Fujian Normal University, Fuzhou 350117, China
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Lu XY, Wang CY, Tang H, Qin YF, Cui L, Wang X, Liu GK, Ren B. Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information. Anal Chem 2024. [PMID: 38324760 DOI: 10.1021/acs.analchem.3c03889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Chen-Yue Wang
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Hui Tang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yi-Fei Qin
- Xiamen Key Laboratory of Indoor Air and Health, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Cui
- Xiamen Key Laboratory of Indoor Air and Health, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiang Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
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