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

Objective

To propose a Light-Atten-Pose-based algorithm for classifying abnormal morphology 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 medicine 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 algorithm. 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 mechanism 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 calculating 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 evaluation 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 robustness, 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.