Figure - available from: Frontiers in Neuroscience
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Samples of correctly recognized CHS categories. (A) Lonicerae japonicae flos. (B) Bletillae rhizoma. (C) Uncariae ramulus cum uncis. (D) Poria.
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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 a...
Citations
... 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. ...
Review The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine Zhipeng Ke 1,2, Minxuan Liu 1,2,3, Jing Liu 1,2, Zhenzhen Su 1,2, Lu Li 1,2, Mengyu Qian 1,2, Xinzhuang Zhang 1,2, Tuanjie Wang 1,2, Liang Cao 1,2, Zhenzhong Wang 1,2, and Wei Xiao 1,2, * 1 National Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Lianyungang 222106, China 2 Jiangsu Kanion Pharmaceutical Co., Ltd, Lianyungang 222104, China 3 School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210009, China * Correspondence: xw_kanion@163.com Received: 4 September 2023 Accepted: 4 November 2023 Published: 6 March 2024 Abstract: With the accumulation of data in the pharmaceutical industry and the development of artificial intelligence technology, various artificial intelligence methods have been successfully employed in the drug discovery process. The integration of artificial intelligence in Traditional Chinese medicine has also gained momentum, encompassing quality control of Chinese patent medicines, prescriptions optimization, discovery of effective substances, and prediction of side effects. However, artificial intelligence also faces challenges and limitations in Traditional Chinese medicine development, such as data scarcity and complexity, lack of interdisciplinary professionals, black-box models, etc. Therefore, more research and collaboration are needed to address these issues and explore the best ways to integrate artificial intelligence and Traditional Chinese medicine to improve human health.
... 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. ...
This article investigated the attention mechanism implemented by the Fully Convolutional Network (FCN) Model on the Kitti Lane Dataset. Two attention mechanisms were applied in the deep learning model to improve traffic lane detection for autonomous vehicles. The Kitti lane dataset, which was generated in collaboration with Jannik Fritsch and Tobias Kuehl from Honda Research Europe GmbH, was selected for this study. The results demonstrate that the applied attention mechanism can effectively improve the network's feature representation on lane markings. Furthermore, this approach can improve the weighted information of lane line targets while decreasing irrelevant information. As a result, the proposed technique improved, obtaining more than 95% accuracy. Subsequently, the attention mechanism was implemented in the FCN model architecture to enhance the lane-detecting model. As a result, in the future, more comprehensive ideas, such as combining the FCN model with Transfer Learning, will play an essential part in investigating the improvement of lane detection areas.
... According to the description of the Pharmacopoeia of the People's Republic of China on the standard of Chinese medicine tablets, the raw materials of Chinese medicine tablets are generally from the roots and stems, bark, flowers, leaves, and fruits of plants [7][8][9]46]. The leaves and flowers of plants are dried and shaped into finished Chinese medicine tablets, the roots and stems of Chinese medicine tablets are mostly made in the form of slices, which are classified into thin slices, thick slices, slanted slices, straight slices, filaments, blocks, etc. ...
... According to the description of the Pharmacopoeia of the People's Republic of China on the standard of Chinese medicine tablets, the raw materials of Chinese medicine tablets are generally from the roots and stems, bark, flowers, leaves, and fruits of plants [7][8][9]46]. The leaves and flowers of plants are dried and shaped into finished Chinese medicine tablets, the roots and stems of Chinese medicine tablets are mostly made in the form of slices, which are classified into thin slices, thick slices, slanted slices, straight slices, filaments, blocks, etc. ...
With the development of computer vision technology, the demand for deploying vision inspection tasks on edge mobile devices is becoming increasingly widespread. To meet the requirements of application scenarios on edge devices with limited computational resources, many lightweight models have been proposed that achieves good performance with fewer parameters. In order to achieve higher model accuracy with fewer parameters, a novel lightweight convolutional neural network CCNNet is proposed. The proposed model compresses the modern CNN architecture with “bottleneck” architecture and gets multi-scale features with downsampling rate 3, adopts GCIR module stacking and MDCA attention mechanism to promote the model performance. Compares with several benchmark lightweight convolutional neural network models on CIFAR-10, CIFAR-100 and ImageNet-1 K, the proposed model outperforms them. In order to verify its generalization, a fine-grained dataset for traditional Chinese medicine recognition named “TCM-100” is created. The proposed model applies in the field of traditional Chinese medicine recognition and achieves good classification accuracy, which also demonstrates it generalizes well. The bottleneck framework of the proposed model has some reference values for the design of lightweight model. The proposed model has some promotion significance for classification or recognition applications in other fields.
Chinese herbal medicine (CHM) is integral to a traditional Chinese medicine (TCM) system. Accurately identifying Chinese herbal medicine is crucial for quality control and prescription compounding verification. However, with many Chinese herbal medicines and some with similar appearances but different therapeutic effects, achieving precise identification is a challenging task. Traditional manual identification methods have certain limitations, including labor-intensive, inefficient. Deep learning techniques for Chinese herbal medicine identification can enhance accuracy, improve efficiency and lower coats. However, few high-quality Chinese herbal medicine datasets are currently available for deep learning applications. To alleviate this problem, this study constructed a dataset (Dataset 1) containing 3,384 images of 20 common Chinese herbal medicine fruits through web crawling. All images are annotated by TCM experts, making them suitable for training and testing Chinese herbal medicine identification methods. Furthermore, this study establishes another dataset (Dataset 2) of 400 images by taking pictures using smartphones to provide materials for the practical efficacy evaluation of Chinese herbal medicine identification methods. The two datasets form a Ningbo Traditional Chinese Medicine Chinese Herb Medicine (NB-TCM-CHM) Dataset. In Dataset 1 and Dataset 2, each type of Chinese medicine herb is stored in a separate folder, with the folder named after its name. The dataset can be used to develop Chinese herbal medicine identification algorithms based on deep learning and evaluate the performance of Chinese herbal medicine identification methods.
Medicinal plants have always been studied and considered due to their high importance for preserving human health. However, identifying medicinal plants is very time-consuming, tedious and requires an experienced specialist. Hence, a vision-based system can support researchers and ordinary people in recognising herb plants quickly and accurately. Thus, this study proposes an intelligent vision-based system to identify herb plants by developing an automatic Convolutional Neural Network (CNN). The proposed Deep Learning (DL) model consists of a CNN block for feature extraction and a classifier block for classifying the extracted features. The classifier block includes a Global Average Pooling (GAP) layer, a dense layer, a dropout layer, and a softmax layer. The solution has been tested on 3 levels of definitions (64 × 64, 128 × 128 and 256 × 256 pixel) of images for leaf recognition of five different medicinal plants. As a result, the vision-based system achieved more than 99.3% accuracy for all the image definitions. Hence, the proposed method effectively identifies medicinal plants in real-time and is capable of replacing traditional methods.
