TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis

• URL: http://arxiv.org/abs/2507.18288v1
• Date: Thu, 24 Jul 2025 10:49:31 GMT
• Title: TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis
• Authors: Xuebo Jin, Longfei Gao, Anshuo Tong, Zhengyang Chen, Jianlei Kong, Ning Sun, Huijun Ma, Qiang Wang, Yuting Bai, Tingli Su,
• Abstract summary: Traditional Chinese medicine (TCM) tongue diagnosis faces standardization challenges due to subjective interpretation and inconsistent imaging protocols.<n>To address this gap, we present the first specialized dataset for AI-driven TCM tongue diagnosis.<n>The dataset includes 6,719 high-quality images captured under standardized conditions and annotated with 20 pathological symptom categories.
• Score: 12.39302160184597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional Chinese medicine (TCM) tongue diagnosis, while clinically valuable, faces standardization challenges due to subjective interpretation and inconsistent imaging protocols, compounded by the lack of large-scale, annotated datasets for AI development. To address this gap, we present the first specialized dataset for AI-driven TCM tongue diagnosis, comprising 6,719 high-quality images captured under standardized conditions and annotated with 20 pathological symptom categories (averaging 2.54 clinically validated labels per image, all verified by licensed TCM practitioners). The dataset supports multiple annotation formats (COCO, TXT, XML) for broad usability and has been benchmarked using nine deep learning models (YOLOv5/v7/v8 variants, SSD, and MobileNetV2) to demonstrate its utility for AI development. This resource provides a critical foundation for advancing reliable computational tools in TCM, bridging the data shortage that has hindered progress in the field, and facilitating the integration of AI into both research and clinical practice through standardized, high-quality diagnostic data.

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