T2Net: Tongue Image-Based T2DM Detection Via Simulated Clinical Diagnostic Reasoning

Clinical studies indicate that the progression of Type 2 Diabetes Mellitus (T2DM) is associated with characteristic alterations in tongue features, which may facilitate non-invasive early detection. However, current deep learning–based tongue imaging approaches for diabetes diagnosis remain constrained by limited datasets, subtle feature variations, dependence on clinical expertise, and the lack of quantitative evaluation. To address these issues, we developed an open-source dataset for T2DM tongue diagnosis (DMT) and benchmarked it using multiple baseline models. Building on DMT, we propose T2Net, a tongue image recognition model for T2DM that simulates the clinical diagnostic process. T2Net comprises four core components: local inspection, pathological clue integration, syndrome identification, and diagnostic confidence estimation. First, T2Net automatically extracts key ROIs by combining large-kernel decomposition with multi-scale learning. Then, a multi-order feature interaction module enables effective fusion of tongue image features across scales to capture pathological clues. Meanwhile, we design a context-aware dynamic aggregation convolution to model long-range dependencies, and propose a flexible focal loss to mimic the diagnostic reasoning process of clinicians, enabling brain-inspired inference. Finally, we propose a clustering-based confidence estimation approach to quantitatively evaluate the reliability of model predictions. Experimental results demonstrate that T2Net achieves highly competitive performance on the DMT dataset, outperforming the second-best baseline by 2.7% in accuracy and 2.0% in F1 score. Moreover, the quantitative evaluation scores are largely consistent with clinical assessments by physicians. DMT dataset is available at https://github.com/yjy-97/DMT.