Abstract
Chinese herbal medicine classification is a critical task in medication distribution and intelligent medicine, as well as a significant topic in computer vision. However, the majority of contemporary mainstream techniques are semiautomatic, with low efficiency and performance. To tackle this problem, a novel Chinese herbal medicine classification approach, Mutual Triplet Attention Learning (MTAL), is proposed. The motivation of our approach is to leverage a group of student networks to learn collaboratively and teach each other about cross-dimension dependencies throughout the training process, with the goal of quickly gaining strong feature representations and improving the outcomes. The results of the experiments show that MTAL outperforms other models in terms of accuracy and computation time. MTAL, in particular, improves accuracy by over 5.5 percent while reducing calculation time by over 50 percent.
Highlights
Two essential optimization goals for image classification, such as the Chinese herbal medicine (CHM) classification, are computational efficiency and classification accuracy
In order to perform Chinese herbal medicine classification and solve the high-dimensional nonlinear problem in the data of CHM, Luo et al [1] developed a new approach that applying the linear discriminant analysis (LDA) and locally linear embedding algorithm (LLE) to conduct the CHM classification, whereas the dataset utilized in their paper is quite small that only includes six classes
In order to further improve the Chinese herbal medicine classification performance in terms of accuracy and calculation time, this paper develops a novel Mutual Triplet Attention Learning (MTAL) approach by integrating the advantages of mutual learning and triplet attention
Summary
Two essential optimization goals for image classification, such as the Chinese herbal medicine (CHM) classification, are computational efficiency and classification accuracy. Unger et al [3] proposed a novel method that leverages the support vector machine (SVM) with morphometric and Fourier characteristics to perform the CHM classification based on two test datasets. These two datasets contain 17 and 26 classes separately, and each category has about 10 samples. Promising performances have been achieved by these methods, the dataset they employed is quite small, and there are few samples in each category These methods based on hand-crafted features with less robustness lead to poor classification performance. The model is large and has many parameters, which restricts their utilization in platforms or applications with fast execution or low memory demands, e.g., mobile phones
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