Prior-guided feature fusion for tongue image-based gastrointestinal disease auxiliary diagnosis

Gastrointestinal diseases (GIDs) are a major global health burden, but traditional endoscopic screening faces limitations due to its invasiveness. Tongue image analysis offers a non‑invasive alternative but is subjective, relying on clinical experience.

To address this, we propose a novel computational framework that synergistically integrates clinical prior knowledge with data-driven learning for non-invasive screening via tongue image analysis. Our framework introduces two key innovations: a TransNeXt hybrid backbone to extract comprehensive local and global tongue features, and a Channel Attention Gated Fusion module that performs asymmetric feature recalibration and prior-guided adaptive integration.

Evaluated on a multicenter gastrointestinal disease tongue image dataset, our framework demonstrates robust performance, with an SVM classifier achieving an accuracy of 0.874, a Macro-F1 score of 0.879, and an AUC of 0.898.

Ablation studies confirm the critical synergy between multimodal feature fusion and data augmentation. This work establishes an effective and objective computational solution for tongue diagnosis, with strong potential for clinical translation as an auxiliary screening tool.