Feng Li, Huang Zong Hai, Zhong Yan Mei, Xiao WenKe, Wen Chuan Biao, Song Hai Bei, Guo Jin Hong
Chengdu University of Traditional Chinese Medicine College of Intelligent Medicine, Chengdu, Sichuan, China.
University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Digit Health. 2022 Sep 19;8:20552076221124436. doi: 10.1177/20552076221124436. eCollection 2022 Jan-Dec.
To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis.
Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application.
Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms.
There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.
探讨深度学习在舌面诊断中的技术研究及应用特点。
通过总结传统医学舌面诊断中当前图像处理技术的优缺点，分析和综述深度学习在舌图像预处理、分割及分类方面的研究现状，并将算法与真实舌面图像进行比较验证。系统回顾了传统医学中用于诊断的面部和舌部图像，从采集与预处理到分割、分类、算法比较、分析结果及应用。
深度学习提高了舌面诊断图像数据处理的速度和准确性。其中，U-net和Seg-net模型的平均交并比超过0.98，分割速度在54至58毫秒之间。
在图像采集条件和图像处理方法方面，舌面诊断客观化尚无统一标准，因此进一步研究不可或缺。通过减少环境等因素对舌面诊断图像质量的影响并提高图像处理效率，在医学图像分析领域使用移动端采集的图像是可行的。
