Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition

Abstract

Chinese Herbal Slices (CHS) are critical components of Traditional Chinese Medicine (TCM); the accurate recognition of CHS is crucial for applying to medicine, production, and education. However, existing methods to recognize the CHS are mainly performed by experienced professionals, which may not meet vast CHS market demand due to time-consuming and the limited number of professionals. Although some automated CHS recognition approaches have been proposed, the performance still needs further improvement because they are primarily based on the traditional machine learning with hand-crafted features, resulting in relatively low accuracy. Additionally, few CHS datasets are available for research aimed at practical application. To comprehensively address these problems, we propose a combined channel attention and spatial attention module network (CCSM-Net) for efficiently recognizing CHS with 2-D images. The CCSM-Net integrates channel and spatial attentions, focusing on the most important information as well as the position of the information of CHS image. Especially, pairs of max-pooling and average pooling operations are used in the CA and SA module to aggregate the channel information of the feature map. Then, a dataset of 14,196 images with 182 categories of commonly used CHS is constructed. We evaluated our framework on the constructed dataset. Experimental results show that the proposed CCSM-Net indicates promising performance and outperforms other typical deep learning algorithms, achieving a recognition rate of 99.27%, a precision of 99.33%, a recall of 99.27%, and an F1-score of 99.26% with different numbers of CHS categories.

NB-TCM-CHM: Image dataset of the Chinese herbal medicine fruits and its application in classification through deep learning

“…Accurate identification and utilization of Chinese herbal medicine are crucial for ensuring the efficacy and safety of traditional Chinese medicine treatments [ 2 , 5 , 6 ]. In recent years, deep learning has achieved outstanding results in image processing.…”

“…In recent years, researchers have gradually used deep learning to recognize Chinese herbal medicines due to significant breakthroughs in image recognition and analysis. Wang et al [2] constructed a deep convolutional neural network combining the channel and spatial attention modules for the recognition of Chinese herbal medicine. Gang et al [3] designed a lightweight convolutional neural network for the identification of Chinese herbal medicines and achieved good classification accuracy.…”

The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine

“…This suggests that deep learning, particularly the lightweight network MobileNetV3, can effectively recognize the stir-frying stage of GFP. Wang et al introduced a CCSM-Net, a novel network based on ResNeSt architecture, combining channel attention (CA) and spatial attention (SA) modules for enhanced recognition of local CHS images [25]. The CCSM-Net focuses on critical information in feature maps by leveraging both channel-wise and SA, with SA reinforcing CA's capabilities in emphasizing spatial information.…”

Fully Convolutional Network Model Applied Attention Mechanism on Kitti Lane Dataset for Lane Detection

“…In the FCN network model architecture, the suggested module employs channel attention and an efficient channel attention block. Because of the diversity of image information in the feature map of the lane, several unnecessary pieces of information should be eliminated before weight computation while still maintaining key texture features to optimize the feature sophistication [49]. The channel attention mechanism represents and evaluates the relevance of each channel using a scalar.…”