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