Prior knowledge-guided adaptive multi-scale deep learning for Surface-Enhanced Raman Spectroscopy-based liver disease classification

Surface-Enhanced Raman Spectroscopy (SERS) combined with deep learning demonstrates considerable potential for liver disease diagnosis. However, acquiring large-scale clinical datasets is challenging due to patient privacy constraints and sample collection complexity, leading to data scarcity that limits deep learning performance. Most existing methods rely heavily on data-driven approaches and fail to effectively utilize prior biomolecular knowledge, making them prone to overfitting. To address these limitations, we present a prior knowledge-guided adaptive multi-scale deep learning model that incorporates literature-validated biomolecular peak positions into feature learning. The model employs a dual-path architecture: an expert-guided path extracts structured features using adaptive multi-scale Gaussian convolutions optimized for distinct biomolecular markers, while a global context path captures comprehensive spectral information. An adaptive fusion mechanism integrates these paths to achieve synergy between prior knowledge and data-driven learning. In a five-class liver disease classification task with 215 subjects, our method achieved 93.66% accuracy, a 5.37% improvement over the baseline convolutional neural network (Baseline CNN, 88.29%). Data constraint experiments demonstrated superior robustness; when training data was reduced to 20%, our approach maintained 86.01% accuracy with a 10.73 percentage point margin over the baseline. Furthermore, independent external validation on a cohort of 35 subjects yielded an overall accuracy of 82.74%, significantly outperforming the Baseline CNN’s 68.23% and reducing the generalization gap from 20.15% to 10.72%, validating the model’s robustness in cross-center clinical scenarios. This work provides an effective integration of domain knowledge with artificial intelligence for Surface-Enhanced Raman Spectroscopy-based medical diagnosis.