Deep learning-empowered Raman spectroscopy for gastric cancer diagnosis: a review of data pipelines and algorithms

Raman spectroscopy is emerging as a label-free tool for gastric cancer diagnosis by capturing molecular fingerprints of malignant transformation. However, weak scattering, strong fluorescence background, anatomical heterogeneity, and limited annotated datasets restrict its translation into routine endoscopic practice. This review examines the integration of deep learning with Raman spectroscopy from a data-pipeline perspective. We analyze core data challenges and summarize learning-based preprocessing strategies for denoising, baseline correction, and spectral reconstruction. Data augmentation approaches-particularly generative models for small-sample and class-imbalance problems-are then reviewed. Major deep learning architectures are categorized by analytical role: convolutional networks extract local spectral patterns, recurrent models capture biochemical sequential dependencies, and Transformer-based models support global context modeling and interpretable visualization. The evidence indicates a clear shift toward data-centric, end-to-end representation learning. Future work should integrate explainable AI with calibration transfer, domain adaptation, standardized validation, and prospective multicenter evaluation to enable clinical optical biopsy.