Multi-label body constitution recognition via dual transform MLP-like architecture using tongue images

Abstract

According to the traditional Chinese medicine composite constitution theory, body constitution recognition is modeled as a unique task of multi-label problems using tongue images. Although MLP-like architecture is one of the base models except for CNN-based, Transformer-based, and MLP-like-based for computer vision tasks, the performance of the MLP-like-based architecture for the multi-label BCR task needs to expand for multi-label BCR task with a specific module using tongue images. Thus, a novel dual transform MLP-like (DT-MLP) model is designed, in which discrete Fourier transform and chaos transform are used for feature extraction of their branches. Besides, a new multi-label tongue body constitution dataset is constructed. The results demonstrate that the proposed DT-MLP outperforms other state-of-the-art MLP-like models on mAP, AUC, and Acc, respectively.