Research on disease diagnosis technology based on the fusion of multi-spectrum matching synergistic attention mechanism in Raman and infrared spectroscopy.

In today's medical diagnosis field, accurate diagnosis of many diseases plays an important role. As emerging non-invasive diagnostic technologies, Raman spectroscopy and infrared spectroscopy have shown unique advantages in the detection of disease characteristic markers due to their advantages such as high sensitivity and specificity. However, the diagnosis of some diseases by using only Raman or infrared single-spectral technology is relatively insufficient. Therefore, how to effectively utilize the two spectral data and accurately diagnose the disease as early as possible has become an important challenge.At present, the fusion of two spectral data types has shown good performance in disease diagnosis. Compared with single-spectral technology, multimodal fusion can more comprehensively reflect disease-related information, thereby improving the accuracy and reliability of diagnosis. However, existing research is usually limited to a single disease, and the inter-modal feature conflicts in the multimodal fusion process have not been effectively addressed. The conflict and overlapping information between the two in certain frequency bands and features are often ignored, and the fusion is mainly focused on shallow levels.Based on the above problems, this paper proposes a disease diagnosis technology based on the fusion of multi-spectral matching and a collaborative attention mechanism of Raman and infrared spectroscopy. Through the frequency band adaptive decomposition mechanism combined with a dynamic weighting strategy of multi-spectral attention, the problems of modality conflict and redundant fusion in traditional methods are effectively solved. Cross-modal deep interaction is then achieved through residual feature injection, allowing for cross-modal complementary enhancement of key features for early diagnosis while maintaining modality specificity.Comprehensive experimental results show that the model has excellent performance in the diagnosis of thyroid cancer and systemic lupus erythematosus(SLE), reaching 98.46 % and 98.57 % accuracy, 98.46 % and 99.00 % recall, and 98.93 % and 98.50 % AUC values, respectively, providing a new method and idea for disease diagnosis using multimodal spectral data.