Research and Application of Tongue Image Segmentation Method Based on Lightweight Deep Learning

Intelligent tongue diagnosis demands high-precision segmentation on resource-constrained mobile devices. All deep learning models outperform conventional approaches, improving mean intersection over union (mIoU) by approximately 9% and accelerating inference by more than two orders of magnitude. Among the evaluated models, UNet++ achieves the highest test mIoU (0.9543) and Dice (0.9764), with paired bootstrap analysis confirming statistically significant advantages over BiSeNet, PP-LiteSeg, and HRNet. BiSeNet achieves the fastest inference speed (310.03 FPS) and the highest mPA (0.9766), making it the most efficient model for real-time deployment. HRNet and PP-LiteSeg provide a favorable accuracy-efficiency balance, suitable for mobile-based tongue diagnosis. Deployment of PP-LiteSeg on three Android devices achieved inference latency of 233-262 ms and throughput of 3.2-3.4 FPS, demonstrating the feasibility of lightweight models on commodity mobile hardware. These findings establish an evidence-based framework for model selection in mobile tongue diagnosis, supporting AI deployment on portable devices for clinical assistance and remote monitoring.