基于文献计量学和文本分析法的智能中医面诊分区方法系统性综述
A Systematic Review of Intelligent TCM Facial Diagnosis Zoning Methods Based on Bibliometrics and Text Analysis
马圆港 1冯跃 1林卓胜 1李胜可 1吴欣 1刘启超 1徐红2
作者信息
- 1. 五邑大学智能制造学部 江门 529020
- 2. 五邑大学智能制造学部 江门 529020;维多利亚大学可持续工业与宜居城市研究院 墨尔本 8001
- 折叠
摘要
面诊客观化研究通过近年来不断发展,已经成为多学科多领域交叉的研究主题之一.但大部分研究注重对面诊图像算法的调整与采集环境及设备的设计,缺乏对面诊分区方法的系统性研究.本文旨在对现阶段基于机器学习的智能中医面诊分区研究文献中存在的问题进行讨论并提出建议,为后续的相关研究提供参考.文章运用文献计量学方法和文本分析法进行梳理分析,整理归纳出目前智能中医面诊分区方法,主要包括基于面部特征点、基于面部特征块和基于完整面部;然后通过分析面诊分区研究的影响因素并对常用的机器学习算法进行归纳,得到不同机器学习算法的优缺点以及对应的常用面诊分区方法;最后对现阶段面诊分区研究中的数据集构建、深度学习的优势以及面诊理论的体现三个方面进行讨论.
Abstract
The objectification of facial diagnosis has been developed through recent years and has become a multidisciplinary research topic.However,many studies are still limited to the adjustment of algorithms and the design of data collection environment and equipments,few studies focus on facial diagnosis zoning.The purpose of this paper is to discuss the problems in the current research literature on machine learning-based intelligent TCM facial diagnosis zoning to build a foundation for subsequent related research.The study uses bibliometric methods and text analysis to clarify and analyze the current intelligent TCM facial diagnosis zoning methods,which mainly include facial feature point-based,facial feature block-based and complete face-based method;then by analyzing the influencing factors of facial diagnosis zoning research and summarizing the common machine learning algorithms,the advantages and disadvantages of different machine learning algorithms and the corresponding common facial diagnosis zoning methods are obtained;Finally,we discuss three aspects of the current phase of facial diagnosis zoning research:dataset construction,advantages of deep learning,and embodiment of facial diagnosis theory.
关键词
文献计量学/系统综述/机器学习/深度学习/智能中医/面诊分区Key words
Bibliometrics/Systematic review/Machine learning/Deep learning/Intelligent TCM/Facial diagnosis zoning methods引用本文复制引用
基金项目
广东省教育厅2021年度广东省普通高校重点领域专项项目(2021ZDZX1032)
广东省科学技术厅广东省国际及港澳台高端人才交流专项(2020A1313030021)
五邑大学科研项目(2018TP023)
出版年
2024
国家科技期刊平台
NSTL
万方数据