Skip to main content

Advertisement

SpringerLink
Log in

Research on disease diagnosis based on teacher-student network and Raman spectroscopy

  • Original Article
  • Published:
Lasers in Medical Science Aims and scope Submit manuscript

Abstract

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren’s syndrome is a disease that poses a major threat to women’s health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren’s syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren’s syndrome.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Gray SP, Jandeleit-Dahm K (May 2014) The pathobiology of diabetic vascular complications—cardiovascular and kidney disease. J Mol Med 92(5):441–452. https://doi.org/10.1007/s00109-014-1146-1

  2. Saudek CD, Herman WH, Sacks DB, Bergenstal RM, Edelman D, Davidson MB (2008) A New Look at Screening and Diagnosing Diabetes Mellitus, The Journal of Clinical Endocrinology & Metabolism, vol. 93, no. 7, pp. 2447–2453, Jul. https://doi.org/10.1210/jc.2007-2174

  3. Laffel L (1999) Ketone bodies: a review of physiology, pathophysiology and application of monitoring to diabetes. Diab/Metab Res Rev 15(6):412–426. https://doi.org/10.1002/(SICI)1520-7560(199911/12)15:6%3C412::AID-DMRR72%3E3.0.CO;2-8

    Article  CAS  Google Scholar 

  4. Lazarus M, Isenberg D (2005) Development of additional autoimmune diseases in a population of patients with primary Sjögren’s syndrome, Ann Rheum Dis, vol. 64, no. 7, pp. 1062–1064, Jul. https://doi.org/10.1136/ard.2004.029066

  5. Negrini S et al (Feb. 2022) Sjögren’s syndrome: a systemic autoimmune disease. Clin Exp Med 22(1):9–25. https://doi.org/10.1007/s10238-021-00728-6

  6. Applications of Raman spectroscopy in cancer diagnosis | Cancer and Metastasis Reviews Accessed: Jan. 18, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s10555-018-9770-9

  7. Herrera LA, Benítez-Bribiesca L, Mohar A, Ostrosky-Wegman P (2005) Role of infectious diseases in human carcinogenesis. Environ Mol Mutagen 45:2–3. https://doi.org/10.1002/em.20122

    Article  CAS  Google Scholar 

  8. Buschman HP et al (Mar. 2001) Diagnosis of human coronary atherosclerosis by morphology-based Raman spectroscopy. Cardiovasc Pathol 10(2):59–68. https://doi.org/10.1016/S1054-8807(01)00063-1

  9. Sbroscia M et al (2020) Aug., Thyroid cancer diagnosis by Raman spectroscopy, Sci Rep, vol. 10, no. 1, Art. no. 1, https://doi.org/10.1038/s41598-020-70165-0

  10. Kavuru V, Senger RS, Robertson JL, Choudhury D (2023) Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies, PeerJ, vol. 11, p. e14879, Feb. https://doi.org/10.7717/peerj.14879

  11. Shinde PP, Shah S, Fourth International Conference on Computing Communication, Control, Automation A (2018) A Review of Machine Learning and Deep Learning Applications, in. 2018, pp. 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697857

  12. Song L et al (2023) Oct., Exploring the Knowledge Transferred by Response-Based Teacher-Student Distillation, in Proceedings of the 31st ACM International Conference on Multimedia, in MM ’23. New York, NY, USA: Association for Computing Machinery, pp. 2704–2713. https://doi.org/10.1145/3581783.3612162

  13. Wang L, Yoon K-J (2022) Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 3048–3068, Jun. https://doi.org/10.1109/TPAMI.2021.3055564

  14. Janke J, Castelli M, Popovič A (2019) Analysis of the proficiency of fully connected neural networks in the process of classifying digital images. Benchmark of different classification algorithms on high-level image features from convolutional layers, Expert Systems with Applications, vol. 135, pp. 12–38, Nov. https://doi.org/10.1016/j.eswa.2019.05.058

  15. Zaras A, Passalis N, Tefas A (2021) Improving knowledge distillation using unified ensembles of specialized teachers, Pattern Recognition Letters, vol. 146, pp. 215–221, Jun. https://doi.org/10.1016/j.patrec.2021.03.014

  16. Ryabchykov O, Guo S, Bocklitz T (Feb. 2019) Analyzing Raman spectroscopic data. Phys Sci Reviews 4(2). https://doi.org/10.1515/psr-2017-0043

  17. Martyna A et al (Jul. 2020) Improving discrimination of Raman spectra by optimising preprocessing strategies on the basis of the ability to refine the relationship between variance components. Chemometr Intell Lab Syst 202:104029. https://doi.org/10.1016/j.chemolab.2020.104029

  18. Sánchez D, Batet M, Isern D, Valls A (Jul. 2012) Ontology-based semantic similarity: a new feature-based approach. Expert Syst Appl 39(9):7718–7728. https://doi.org/10.1016/j.eswa.2012.01.082

  19. Wang S et al (Dec. 2021) Prospects Inform Fusion 76:376–421. https://doi.org/10.1016/j.inffus.2021.07.001. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges

  20. Mirzaei A, Pourahmadi V, Soltani M, Sheikhzadeh H (2020) Deep feature selection using a teacher-student network, Neurocomputing, vol. 383, pp. 396–408, Mar. https://doi.org/10.1016/j.neucom.2019.12.017

  21. Hui L, Cheng M, Xie J, Yang J, Cheng M-M (2022) Efficient 3D point cloud feature learning for large-scale place recognition. IEEE Trans Image Process 31:1258–1270. https://doi.org/10.1109/TIP.2021.3136714

    Article  PubMed  Google Scholar 

  22. Zhang Z-M, Chen S, Liang Y-Z (2010) Baseline correction using adaptive iteratively reweighted penalized least squares, Analyst, vol. 135, no. 5, pp. 1138–1146, Apr. https://doi.org/10.1039/B922045C

  23. Buzzi-Ferraris G, Manenti F (Feb. 2011) Outlier detection in large data sets. Comput Chem Eng 35(2):388–390. https://doi.org/10.1016/j.compchemeng.2010.11.004

  24. Blakeney C, Li X, Yan Y, Zong Z (2021) Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression, IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7, pp. 1765–1776, Jul. https://doi.org/10.1109/TPDS.2020.3047003

  25. Zhang L, Bao C, Ma K (2022) Self-Distillation: Towards Efficient and Compact Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 8, pp. 4388–4403, Aug. https://doi.org/10.1109/TPAMI.2021.3067100

  26. Wang Y, Zhang Z, Lin Y (2022) Multi-Cluster Feature Selection Based on Isometric Mapping, IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 3, pp. 570–572, Mar. https://doi.org/10.1109/JAS.2021.1004398

  27. Shi S, Xu Y, Xu X, Mo X, Ding J (2023) A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding, Entropy, vol. 25, no. 7, Art. no. 7, Jul. https://doi.org/10.3390/e25071065

  28. Roweis ST, Saul LK (2000) Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol. 290, no. 5500, pp. 2323–2326, Dec. https://doi.org/10.1126/science.290.5500.2323

  29. Lee JH, McDonnell KT, Zelenyuk A, Imre D, Mueller K (2014) A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multidimensional Scaling, IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 3, pp. 351–364, Mar. https://doi.org/10.1109/TVCG.2013.101

  30. Bengio Y, Delalleau O, Roux NL, Paiement J-F, Vincent P, Ouimet M (2004) Learning Eigenfunctions Links Spectral Embedding and Kernel PCA, Neural Computation, vol. 16, no. 10, pp. 2197–2219, Oct. https://doi.org/10.1162/0899766041732396

  31. Rana A, Singh Rawat A, Bijalwan A, Bahuguna H (2018) Application of Multi Layer (Perceptron) Artificial Neural Network in the Diagnosis System: A Systematic Review, in International Conference on Research in Intelligent and Computing in Engineering (RICE), Aug. 2018, pp. 1–6. https://doi.org/10.1109/RICE.2018.8509069

  32. Park Y-S, Lek S (2016) Chapter 7 - Artificial Neural Networks: Multilayer Perceptron for Ecological Modeling, in Developments in Environmental Modelling, vol. 28, S. E. Jørgensen, Ed., in Ecological Model Types, vol. 28., Elsevier, pp. 123–140. https://doi.org/10.1016/B978-0-444-63623-2.00007-4

  33. Zhang F et al (Jan. 2021) Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy. Ren Fail 43(1):980–992. https://doi.org/10.1080/0886022X.2021.1937219

  34. Sharma A et al (Jul. 2023) Ameliorating diabetes-associated atherosclerosis and diabetic nephropathy through modulation of soluble guanylate cyclase. Front Cardiovasc Med 10:1220095. https://doi.org/10.3389/fcvm.2023.1220095

  35. Martín-Gallán P, Carrascosa A, Gussinyé M, Domínguez C (2003) Biomarkers of diabetes-associated oxidative stress and antioxidant status in young diabetic patients with or without subclinical complications, Free Radical Biology and Medicine, vol. 34, no. 12, pp. 1563–1574, Jun. https://doi.org/10.1016/S0891-5849(03)00185-0

  36. Johnson AA, Stolzing A (2019) The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell 18(6):e13048. https://doi.org/10.1111/acel.13048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li Z et al (2022) Jun., Analysis of the saliva metabolic signature in patients with primary Sjögren’s syndrome, PLOS ONE, vol. 17, no. 6, p. e0269275, https://doi.org/10.1371/journal.pone.0269275

  38. Yin J et al (2022) CYP51-mediated cholesterol biosynthesis is required for the proliferation of CD4 + T cells in Sjogren’s syndrome. Clin Exp Med Nov. https://doi.org/10.1007/s10238-022-00939-5

    Article  Google Scholar 

  39. Yang Y, Huang S (2014) Suitability of five cross validation methods for performance evaluation of nonlinear mixed-effects forest models – a case study, Forestry: An International Journal of Forest Research, vol. 87, no. 5, pp. 654–662, Dec. https://doi.org/10.1093/forestry/cpu025

Download references

Funding

This work was supported by Xinjiang Uygur Autonomous Region Youth Science Foundation Project(2022D01C695), the Distinguished Young Talents Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region (2022D01E11) , Tianshan Talent-Young Science and Technology Talent Project?NO.2022TSYCJC0033 and 2022TSYCCX0060).

Author information

Authors and Affiliations

  1. College of Software, Xinjiang University, Urumqi, Xinjiang, 830046, China

    Zishuo Chen & Cheng Chen

  2. College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, 830046, China

    Xuecong Tian & Chen Chen

Authors
  1. Zishuo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuecong Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zishuo Chen: investigation, conceptualization, methodology, data collation, software, manuscript writing. Xuecong Tian: experimental supplement, manuscript revision. Chen Chen: investigation, resources, project administration. Cheng Chen: review of experimental results, manuscript revision, supervision, funding acquisition.

Corresponding author

Correspondence to Cheng Chen.

Ethics declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Informed consent

All samples were obtained from the People’s Hospital of the Xinjiang Uygur Autonomous Region. All samples had personal informed consent and obtained ethical approval.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Tian, X., Chen, C. et al. Research on disease diagnosis based on teacher-student network and Raman spectroscopy. Lasers Med Sci 39, 129 (2024). https://doi.org/10.1007/s10103-024-04078-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10103-024-04078-z

Keywords

Search

Navigation