OrientTongue: an oriented and attention-enhanced framework for fine-grained tongue diagnosis

Tongue diagnosis in Traditional Chinese Medicine contains rich clinical information, yet conventional visual assessment remains subjective and poorly standardized. To address key challenges in automated tongue image analysis—small-scale targets, low-contrast lesions, background interference, and strong directional variations—we propose an enhanced YOLOv8-based end-to-end detection framework. The model integrates Bottleneck Transformer (BoT3) modules in the backbone to strengthen global dependency modeling, and inserts Convolutional Block Attention Module (CBAM) attention in the detection head to improve feature discrimination under noisy and low-contrast conditions. To better capture irregular and oriented tongue features, we adopt oriented bounding boxes and design an adaptive NMS strategy that adjusts suppression thresholds based on object scale, improving both small-lesion recall and large-object precision. Experiments on the Tongue-det dataset covering seven clinically relevant tongue phenotypes show an mAP@0.5 of 0.581, with most categories achieving sample-level F1 scores above 0.75. Ablation studies confirm consistent performance gains from each component, especially for subtle and low-contrast features such as rotten coating. Overall, the framework enhances accuracy, robustness, and interpretability, providing a promising pathway toward objective and intelligent tongue diagnosis.