World Health Organization. Suicides around the world in 2019: Global Health Estimates. Suicides around the world in 2019: Global Health Estimates. (2021).
GBD 2019 Mental Disorder Collaborator. Global, regional and national burdens of 12 mental disorders in 204 countries and territories from 1990 to 2019: A systematic analysis for the global burden in 2019. Lancet Psychiatry. 9 (2), 137–150 (2022).
Insel, Tr Rethinking Schizophrenia. Nature 468 (7321), 187–193 (2010).
Cohen, et al. Digital phenotypes of negative symptoms: Relationship to clinician evaluation. Schizophrenia. Bull. 47 (1), 44–53 (2021).
Dellazizzo, L., Potvin, S., Phraxayavong, K. & Dumais, A. A one-year randomized trial comparing cognitive and self-reliant therapy in patients with treatment-resistant schizophrenia. NPJ Schizophrenia. 7 (1), 9 (2021).
Parachalil, Dr, McIntyre, J. & Byrne, the potential of HJ in Raman spectroscopy for the analysis of plasma/serum in liquid state: recent advances. anus. Bioanal. Chemistry. 412 (9), 1993–2007 (2020).
Wang, X., Huang, S., Hu, S., Yan, S. &Ren, B. Basic understanding and application of plasmon-enhanced Raman spectroscopy. nut. Review Phys. 2 (5), 253–271 (2020).
Kaznowska, E. et al. Lung cancer classification and magnitude of malignancy based on FTIR, PCA-LDA analysis, and physics-based computational models. Taranta 186337–345 (2018).
Ghassemi, M. et al. Diagnosis of normal and malignant human gastric tissue samples by FTIR spectra combined with mathematical models. J. Mol. struct. 1229129493 (2021).
Google Scholar
Kuang, L. Etal. Rapid identification of poor horse oil products based on deep learning infrared spectroscopy detection methods. Spectrochim. Acta Part A Mol. Biomol. Spectroscopy. 330125604 (2025).
Google Scholar
Xin, X. Etal. A method for accurate identification of Uighur drugs based on Raman spectroscopy and multi-label deep learning. Spectrochim. Acta Part A Mol. Biomol. Spectroscopy. 315124251 (2024).
Google Scholar
Srivastava, S., Wang, W., Zhou, W., Jin, M. & Vikesland, Detection for PJ Machine Learning-assisted Surface Enhanced Raman Spectroscopy Detection: a Review. environment. SCI. Technor. 58 (47), 20830–20848 (2024).
Yu, S. Etal. Analysis of Raman spectra by using deep learning methods to identify marine pathogens. anus. Chemistry. 93 (32), 11089–11098 (2021).
Chen, A. Etal. Raman spectroscopy combines selective state-space algorithms to construct rapid disease diagnostic models105375 (Chemical Surveying and Intelligent Lab Systems, 2025).
Shi, T. Etal. Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning. SCI. manager 14 (1), 15056 (2024).
Song, H. et al. Multimodal separation and cross-fusion network based on Raman and FTIR spectroscopy for the diagnosis of thyroid malignant tumor metastasis. SCI. manager 14 (1), 29125 (2024).
Zhou, X., Chen, C., Zuo, E., Chen, C. & LV, X. A multimodal model of cross-branch joint attention network based on Raman and FTIR spectroscopy for the diagnosis of multiple selected cancers. Appl. Soft computer. 166112204 (2024).
Google Scholar
Chen, Z. Etal. Diagnose the degree of differentiation between oral cancer types based on extremely deep neural network models and Raman spectroscopy. Spectroscopy. Rhett. 57 (5), 271–283 (2024).
Google Scholar
Chen, C. Etal. Diagnosis of systemic lupus erythematosus using cross-modal-specific transmission fusion techniques based on infrared spectra and metabolomics. anus. T-m. ACTA. 1330343302 (2024).
Zhou, X. Etal. CMACF: A trans-based cross-modal attentional fusion model for systemic lupus erythematosus diagnosis combining Raman spectroscopy, FTIR spectroscopy, and metabolomics. Inf. process. manager. 61 (6), 103804 (2024).
Google Scholar
Feng, Y. Etal. A new technology for structural distance features of Raman spectra and convolutional neural networks for alcohol dependence diagnosis. Microchem. J. 189108485 (2023).
Google Scholar
Wang, M., Wang, W., Zhang, X. &iu, New fault diagnosis of HHC Markov transition field and rolling bearings based on CNN. entropy twenty four (6), 751 (2022).
Yan, J., Kan, J. &Luo, H. Fault diagnosis based on Markov transition fields and residual networks. sensor twenty two (10), 3936 (2022).
He, S. Investigation of cubic spline smoothing based on genetic algorithms for baseline correction of other Raman spectra. Chemical Mettle. Intel. Lab. syst. 1521–9 (2016).
Google Scholar
Optimal removal of Raman spectra from Barton, SJ, Ward, TE & Hennelly, BM algorithms. anus. method. 10 (30), 3759–3769 (2018).
Google Scholar
Movasaghi, Z., Rehman, S. &Rehman, IU Raman spectroscopy of biological tissues. Appl. Spectroscopy. Pastor 42 (5), 493–541 (2007).
Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. nut., 521(7553), 436–444. (2015).
Google Scholar
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). (2016).
Xie, S., Girshick, R., Dollár, P., Tu, Z. &He, K. Aggregated residual transformations of deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1492–1500). (2017).
Simonyan, K. & Zisserman, A. Very deep convolutional network for large-scale image recognition. arxiv preprint (2014). Arxiv: 1409.1556.
Zhang, Y., Zhou, D., Chen, S., Gao, S. & Ma, Y. A crowd of single images via multi-column convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 589–597). (2016).
Huang, J. &Ling uses AUC and accuracy in the evaluation of CX learning algorithms. IEEE Transformer. Knowledge. Data Eng. 17 (3), 299–310 (2005).
Google Scholar
