# A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification
## 1 Introduction  
With the rapid development of artificial intelligence, an increasing number of studies have focused on applying intelligent algorithms to the healthcare domain [1, 2, 3]. Traditional Chinese Medicine(TCM), as an essential component of the global healthcare system, plays a crucial role in medical practices worldwide. Chinese herbal medicines, the core elements of TCM, have demonstrated remarkable efficacy in regulating bodily functions, enhancing the immune system, promoting blood circulation, alleviating fatigue symptoms, and preventing chronic diseases. However, the classification of Chinese herbal medicines faces several challenges: the wide variety and morphological diversity of herbs, the existence of multiple subspecies within the same species, high similarity in morphological features among different herbs, and significant morphological variations due to growth stages, processing methods, and storage conditions. Traditional methods of herbal identification rely heavily on the expertise of professionals [4], which are not only inefficient and costly but also prone to subjectivity and high error rates. Automated classification of Chinese herbal medicines can significantly improve efficiency and reduce the cost of identification, providing robust technical support for the modernization and internationalization of TCM. Image datasets of Chinese herbal medicines are fundamental to automated classification research. A dataset with a wide variety of species, sufficient samples, diverse morphologies, multiple shooting angles, and complex backgrounds is crucial for building classification models with high generalization capabilities. Previous studies have made some progress in this area. For instance, [PERSON] et al. constructed a dataset containing images of six Chinese herbal medicines [5]; [PERSON] et al. developed a dataset with images of 11 Thai herbs [6]; [PERSON] et al. established a dataset with images of 60 types of Chinese herbal slices [7]. Additionally, [PERSON] et al. created a dataset with images of five herbs [8]. In our previous work, we developed the CHMD1 dataset [9], which includes 16 types of Chinese herbal medicine with 3552 sample images, providing a valuable data foundation for subsequent research. Convolutional Neural Networks (CNNs) have been widely applied in computer vision due to their ability to automatically extract image features, effectively overcoming the limitations of traditional manual feature extraction [10; 11; 12; 13]. For example, [PERSON] et al. demonstrated the effectiveness of CNNs in feature extraction through deconvolution network visualization techniques, providing a significant basis for further research [14]. In the field of herbal image classification, researchers have made notable progress using CNNs. [PERSON] et al. achieved advanced classification performance on a six-class Chinese herb dataset using an improved CNN architecture [5], and [PERSON] et al. obtained similar high-efficiency results [6]. Furthermore, [PERSON] et al. employed PointNet to classify seven types of Chinese herbal medicine, further validating the potential of deep learning methods [15]. Transfer learning has also been widely applied, such as [PERSON] et al. using the pre-trained ResNet-50 model [16; 17], and [PERSON] et al. and [PERSON] et al. employing ImageNet pre-trained CNNs for herb classification, all achieving excellent results [8; 18]. These studies fully demonstrate the significant application value of CNNs in Chinese herbal image analysis. Despite the progress in datasets and methods for Chinese herbal classification, two challenges remain. (1) Existing Chinese herbal image datasets generally suffer from small sample sizes, insufficient categories, monotonous backgrounds, and excessive inter-class differences. These issues severely limit the generalization capabilities of models, particularly in handling inter-class similarities and morphological diversity.