数字中医药（英文）2024，Vol.7Issue(3)：256-263,8.DOI:10.1016/j.dcmed.2024.12.005
基于轻量化和极化自注意力机制的中医望诊异常形态分类算法研究
Lightweight and polarized self-attention mechanism for abnormal morphology classification algorithm during traditional Chinese medicine inspection
摘要
Abstract
Objective To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphol-ogy in traditional Chinese medicine(TCM)inspection to solve the problem of relying on manual labor or expensive equipment with personal subjectivity or high cost. Methods First,this paper establishes a dataset of abnormal morphology for Chinese medi-cine diagnosis,with images from public resources and labeled with category labels by several Chinese medicine experts,including three categories:normal,shoulder abnormality,and leg abnormality.Second,the key points of human body are extracted by Light-Atten-Pose algo-rithm.Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention(PSA)mechanism on the basis of AlphaPose,which reduces the computation amount by using EfficientNet network,and the data is finely processed by using PSA mecha-nism in spatial and channel dimensions.Finally,according to the theory of TCM inspection,the abnormal morphology standard based on the joint angle difference is defined,and the classification of abnormal morphology of Chinese medical diagnosis is realized by calculat-ing the angle between key points.Accuracy,frames per second(FPS),model size,parameter set(Params),and giga floating-point operations per second(GFLOPs)are chosen as the eval-uation indexes for lightweighting. Results Validation of the Light-Atten-Pose algorithm on the dataset showed a classification accuracy of 96.23%,which is close to the original AlphaPose model.However,the FPS of the improved model reaches 41.6 fps from 16.5 fps,the model size is reduced from 155.11 MB to 33.67 MB,the Params decreases from 40.5 M to 8.6 M,and the GFLOPs reduces from 11.93 to 2.10. Conclusion The Light-Atten-Pose algorithm achieves lightweight while maintaining high ro-bustness,resulting in lower complexity and resource consumption and higher classification accuracy,and the experiments prove that the Light-Atten-Pose algorithm has a better overall performance and has practical application in the pose estimation task.关键词
中医望诊/异常形态/姿态估计/轻量化/极化自注意力机制Key words
Traditional Chinese medicine(TCM)inspection/Abnormal morphology/Pose estimation/Lightweight/Polarized self-attention(PSA)mecha-nism引用本文复制引用
张琪,胡孔法,王天舒,杨涛..基于轻量化和极化自注意力机制的中医望诊异常形态分类算法研究[J].数字中医药（英文）,2024,7(3):256-263,8.基金项目
National Key Research and Development Program of China(2022YFC3502302). （2022YFC3502302）
