Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks

Li, Jiawei; Zhang, Zhidong; Zhu, Xiaolong; Zhao, Yunlong; Ma, Yuhang; Zang, Junbin; Li, Bo; Cao, Xiyuan; Chen, Xue

doi:10.3390/mi13040501

Cited by 23 publications

(14 citation statements)

References 30 publications

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“…High-resolution tongue images containing non-tongue regions consume a lot of computational resources in the deep learning network. The tongue region extraction method proposed by Li [ 34 ] was employed to greatly reduce the area of non-tongue regions. This method improved the efficiency of subsequent tongue segmentation, while reducing the computational load of the deep learning network.…”

Section: Methodsmentioning

confidence: 99%

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Zhu

Jiu-zhang

et al. 2022

Micromachines

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Gastric cancer has become a global health issue, severely disrupting daily life. Early detection in gastric cancer patients and immediate treatment contribute significantly to the protection of human health. However, routine gastric cancer examinations carry the risk of complications and are time-consuming. We proposed a framework to predict gastric cancer non-invasively and conveniently. A total of 703 tongue images were acquired using a bespoke tongue image capture instrument, then a dataset containing subjects with and without gastric cancer was created. As the images acquired by this instrument contain non-tongue areas, the Deeplabv3+ network was applied for tongue segmentation to reduce the interference in feature extraction. Nine tongue features were extracted, relationships between tongue features and gastric cancer were explored by using statistical methods and deep learning, finally a prediction framework for gastric cancer was designed. The experimental results showed that the proposed framework had a strong detection ability, with an accuracy of 93.6%. The gastric cancer prediction framework created by combining statistical methods and deep learning proposes a scheme for exploring the relationships between gastric cancer and tongue features. This framework contributes to the effective early diagnosis of patients with gastric cancer.

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Section: Methodsmentioning

confidence: 99%

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Zhu

Jiu-zhang

et al. 2022

Micromachines

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“…The selection of baseline characteristics is based on individuals aged ≥ 45 who meet any of the following criteria, which are indicative of a high-risk profile for gastric cancer: 1) Long-term residence in high-incidence areas of gastric cancer; 2) Hp infection; 3) History of chronic atrophic gastritis, gastric ulcer, gastric polyp, residual stomach after surgery, hypertrophic gastritis, pernicious anemia, or other precancerous diseases of the stomach; 4) First-degree relatives with a history of gastric cancer; 5) Presence of other high-risk factors for gastric cancer such as high salt intake, pickled diet, smoking, and heavy alcohol consumption [25][26][27][28]. Since our screening is conducted in high-risk areas, we ignore the first criterion.…”

Section: Comparison Of the Aitonguequiry Model Single Modality Models...mentioning

confidence: 99%

Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

Wang,

Zhang,

Zhang

et al. 2024

Preprint

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Objective: Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy. Design: From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. Results: A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for PLGC (95% confidence interval (CI) 0.71 to 0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82 to 0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone, and also demonstrated superior performance compared to existing screening methodologies. In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC. Conclusion: Our AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden.

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“…These schemes have been used mainly in classification problems [ 23 ], object detection [ 24 ], image processing [ 25 ], and to estimate sustainable transport demand [ 26 ], controllers [ 27 , 28 ], melanoma detection [ 29 ], among others.…”

Section: Preliminariesmentioning

confidence: 99%

Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks

et al. 2022

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Around the world many people loss a body member for many reasons, where advances of technology may be useful to help these people to improve the quality of their lives. Then, designing a technologically advanced prosthesis with natural movements is worthy for scientific, commercial, and social reasons. Thus, research of manufacturing, designing, and signal processing may lead up to a low-cost affordable prosthesis. This manuscript presents a low-cost design proposal for an electromyographic electronic system, which is characterized by a neural network based process. Moreover, a hand-type prosthesis is presented and controlled by using the processed electromyographic signals for a required particular use. For this purpose, the user performs several movements by using the healthy-hand to get some electromyographic signals. After that, the obtained signals are processed in a neural network based controller. Once an usable behavior is obtained, an exact replica of controlled motions are adapted for the other hand by using the designed prosthesis. The characterization process of bioelectrical signals was performed by training twenty characteristics obtained from the original raw signal in contrast with other papers in which seven characteristics have been tested on average. The proposed model reached a 95.2% computer test accuracy and 93% accuracy in a real environment experiment. The platform was tested via online and offline, where the best response was obtained in the online execution time.

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Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks

Cited by 23 publications

References 30 publications

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks

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