A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification

Abstract

Due to the low quality of existing Chinese herbal medicine datasets and the lack of recognition algorithms for herbal images, automatic classification of Chinese herbal medicine is ineffective. In this paper, we constructed a comprehensive dataset comprising 4485 images across 20 categories of Chinese herbal medicine. This dataset captures the morphological diversity of Chinese herbal medicine while reducing inter-class variations and closely mimics real-world complexity. Considering the subtle differences among the data, we proposed a multi-scale feature extraction architecture called MSPyraNet. This architecture is composed of multiple FACNBlock units, which are designed explicitly for herbal medicine characteristics. FACNBlock utilizes a multi-scale representation module, using convolutions and atrous convolutions of varying sizes to generate and fuse multi-scale feature maps. Experimental results show that MSPyraNet improves accuracy by more than 4.72% and 4.54% compared to existing SOTA models on two datasets. Ablation studies validate the effectiveness of the multi-scale representation module. Furthermore, we discovered that MSPyraNet achieves a notable improvement in classifying Chinese herbal medicines that are morphologically similar but belong to different categories. Briefly, this study provides a dataset and methodological reference for future research on Chinese herbal medicine classification.

Keywords

• image recognition
• deep learning
• convolutional neural networks
• Chinese herbal medicine