Lightweight and polarized self-attention mechanism for abnormal morphology classification algorithm during traditional Chinese medicine inspection

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

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.

目的提出一种基于Light-Atten-Pose的中医望诊异常形态分类算法,以解决依靠人工或昂贵的设备具有个人主观性或成本较高的问题。方法首先,本文建立了一个中医望诊异常形态数据集,图像来自公开资源并由多位中医专家标注类别标签,包括正常、肩部异常、腿部异常三类。其次,通过Light-Atten-Pose算法提取人体关键点,Light-Atten-Pose算法在AlphaPose的基础上使用轻量级的EfficientNet网络和极化自注意力(PSA)机制,使用EfficientNet网络降低计算量,使用PSA机制在空间维度和通道维度对数据精细处理。最后,根据中医望诊理论,定义基于关节角度差的异常形态标准,通过计算关键点间的角度,实现对中医望诊异常形态的分类。选择准确度、每秒传输帧数、模型大小、参数量、浮点运算数作为轻量化的评价指标。结果在数据集验证Light-Atten-Pose算法的结果显示,其分类准确度为96.23%,与原Alpha-Pose模型接近。但改进模型的每秒传输帧数达由16.5 fps达到41.6 fps,模型大小由155.11 MB减少至33.67 MB,参数量由40.5 M下降至8.6 M,浮点运算数由11.93减少至2.10。结论Light-Atten-Pose算法实现轻量化的同时保持了较高的鲁棒性,使得复杂度和资源消耗降低,并且有较高的分类准确度,实验证明Light-Atten-Pose算法整体性能更优,在姿态估计任务中具有实际应用价值。