Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network.

When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese medicines. OBJECTIVE: To study the identification algorithm of traditional Chinese medicinals (TCM) microscopic images based on convolutional neural network (CNN) to improve the objectivity and accuracy of microscopic image identification of TCM powders. METHODS: Microscopic image datasets of 4 TCM powders sclereids of Rhizoma Coptidis, Cortex Magnoliae Officinalis, Cortex Phellodendri Chinensis, and Cortex Cinnamomi were constructed, and 400 collected images, as the model training and testing objects, were identified and classified by AlexNet model, VGGNet-16, VGGNet-19, and GoogLeNet model. RESULTS: The average recognition accuracy in the tested microscopic image of AlexNet model, VGGNet-16, VGGNet-19, and the GoogLeNet model is 93.50%, 95.75%, 95.75%, and 97.50% correspondingly. CONCLUSION: The GoogLeNet model has a higher classification accuracy and is the best model to achieve real-time. Applying the CNN to the identification of microscopic images of TCM powders makes the operation of TCM identification simpler and the measurement more accurate while improving repeatability.

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application.

Tongue diagnosis is an essential technique of traditional Chinese medicine (TCM) diagnosis, and the need for objectifying tongue images through image processing technology is growing. The present study provides an overview of the progress made in tongue objectification over the past decade and compares segmentation models. Various deep learning models are constructed to verify and compare algorithms using real tongue image sets. The strengths and weaknesses of each model are analyzed. The findings indicate that the U-Net algorithm outperforms other models regarding precision accuracy (PA), recall, and mean intersection over union (MIoU) metrics. However, despite the significant progress in tongue image acquisition and processing, a uniform standard for objectifying tongue diagnosis has yet to be established. To facilitate the widespread application of tongue images captured using mobile devices in tongue diagnosis objectification, further research could address the challenges posed by tongue images captured in complex environments.

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia.

A systematic review and meta-analysis were conducted to evaluate the clinical effectiveness and safety of Shugan Jieyu capsules for treating insomnia by searching seven databases up to February 21, 2022. The study was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of the studies was assessed using the risk of bias assessment tool. This article describes in detail how to retrieve and screen the literature. The detailed steps for conducting the meta-analysis are also included in the protocol. Fourteen studies were found to be eligible, including 1,283 insomnia patients (644 with and 639 without Shugan Jieyu capsules at baseline). The meta-analysis showed a better total clinical effectiveness (odds ratio [OR]: 5.71, 95% confidence interval [CI]: 3.56 to 9.15) and a lower Pittsburgh Sleep Quality Index (PSQI) score (mean difference [MD]: -2.95, 95% CI: -4.97 to -0.93) with combined Shugan Jieyu capsules and Western medicine compared to Western medicine alone. The secondary outcomes showed that the Shugan Jieyu capsule group had significantly reduced adverse reactions and improvements in sleep duration, night awakening, nightmares with excessive dreaming, daytime sleepiness, and low energy. Further multicenter randomized trials must be encouraged to provide more concrete evidence on whether Shugan Jieyu capsules are beneficial in routine clinical practice.

RAFF-Net: An improved tongue segmentation algorithm based on residual attention network and multiscale feature fusion.

Objective: Due to the complexity of face images, tongue segmentation is susceptible to interference from uneven tongue texture, lips and face, resulting in traditional methods failing to segment the tongue accurately. To address this problem, RAFF-Net, an automatic tongue region segmentation network based on residual attention network and multiscale feature fusion, was proposed. It aims to improve tongue segmentation accuracy and achieve end-to-end automated segmentation. Methods: Based on the UNet backbone network, different numbers of ResBlocks combined with the Squeeze-and-Excitation (SE) block was used as an encoder to extract image layered features. The decoder structure of UNet was simplified and the number of parameters of the network model was reduced. Meanwhile, the multiscale feature fusion module was designed to optimize the network parameters by combining a custom loss function instead of the common cross-entropy loss function to further improve the detection accuracy. Results: The RAFF-Net network structure achieved Mean Intersection over Union (MIoU) and F1-score of 97.85% and 97.73%, respectively, which improved 0.56% and 0.46%, respectively, compared with the original UNet; ablation experiments demonstrated that the improved algorithm could contribute to the enhancement of tongue segmentation effect. Conclusion: This study combined the residual attention network with multiscale feature fusion to effectively improve the segmentation accuracy of the tongue region, and optimized the input and output of the UNet network using different numbers of ResBlocks, SE block, multiscale feature fusion and weighted loss function, increased the stability of the network and improved the overall effect of the network.