Sle Diagnosis Research Based on Sers Combined with a Multi-Modal Fusion Method

Abstract

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for Systemic Lupus Erythematosus (SLE), the relatively weak signals from a single Raman spectroscopic data make it difficult to obtain desirable classification results. Although the surface enhancement technique can enhance the scattered signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the non-uniform distribution of hotspots degrade some of the signals. To fully utilize both types of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique that allows the original spectra to complement the degraded portion, improving the model's classification accuracy. The residual module is used to extract features, which preserves the original features while extracting the deep ones. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight assignment of the two modal features more efficiently. The experimental results demonstrated that both the low-level fusion method and the intermediate-level fusion method significantly improved the diagnostic accuracy of SLE disease classification when compared with a single modality, and the intermediate-level fusion DBRAN achieved 100% classification accuracy, sensitivity, and specificity. Compared with the raw Raman spectrum single mode and SERS single mode, the improvement is 10% and 7%, respectively. This experiment realized the rapid diagnosis of SLE disease by fusing the spectral data of multiple modes, which provides a reference idea in the Raman spectroscopy field.

Note:
Funding Declaration: This work was supported by Tianshan Talent-Young Science and Technology Talent Project(2022TSYCCX0060), Xinjiang Uygur Autonomous Region Youth Science Foundation Project(2022D01C695).

Conflicts of Interest: None

Ethical Approval: The serum samples were sourced from Xinjiang Uygur Autonomous Region People's Hospital, with all analyses conducted under exempted informed consent and approval from the hospital's Ethics Committee.

Keywords: Artificial intelligence techniques, Raman spectroscopy, surface enhancement techniques, SLE, DBRAN fusion models