Tongue Fissure Visualization with Deep Learning

被引:7
作者
Chang, Wen-Hsien [1 ]
Chu, Hsueh-Ting [2 ]
Chang, Hen-Hong [3 ,4 ]
机构
[1] China Med Univ, Sch Postbaccalaureate Chinese Med, Taichung, Taiwan
[2] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan
[3] China Med Univ, Grad Inst Integrated Med, Sch Postbaccalaureate Chinese Med, Taichung, Taiwan
[4] China Med Univ Hosp, Dept Chinese Med, Taichung, Taiwan
来源
2018 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | 2018年
关键词
Chinese medicine; tongue diagnosis; artificial intelligence; deep learning; class activation mapping;
D O I
10.1109/TAAI.2018.00013
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Tongue diagnosis is a unique practice in traditional Chinese medicine(TCM), which can be used to infer the health condition of a person. However, different TCM doctors may give different interpretations on the same tongue. If an artificial intelligence model can be developed based on a large number of doctor-interpreted tongue images, a more objective judgment will be obtained. Deep learning in artificial intelligence has excellent performance in image recognition, and feature extraction can be done automatically by deep learning without image processing experts. This study attempts to develop a deep learning model through a large number of tongue images, especially for tongue fissures. We also visualize the fissure regions with Gradient-weighted Class Activation Mapping(Gradcam). Therefore, the model not only try to detect tongue fissures but also localize tongue fissure regions.
引用
收藏
页码:14 / 17
页数:4
相关论文
共 7 条
  • [1] [Anonymous], 2010 INT COMP S ICS2
  • [2] [Anonymous], 2017 IEEE 2 INT C BI
  • [3] [Anonymous], EVIDENCE BASED COMPL
  • [4] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [5] Review on the current trends in tongue diagnosis systems
    Jung, Chang Jin
    Jeon, Young Ju
    Kim, Jong Yeol
    Kim, Keun Ho
    [J]. INTEGRATIVE MEDICINE RESEARCH, 2012, 1 (01) : 13 - 20
  • [6] Deep learning in neural networks: An overview
    Schmidhuber, Juergen
    [J]. NEURAL NETWORKS, 2015, 61 : 85 - 117
  • [7] Selvan MP, 2017, 2017 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES)