TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

Huang, Xiaodong; Zhang, Hui; Li, Zhuo; Li, Xiaoguang

doi:10.1155/2020/6029258

Cited by 18 publications

(13 citation statements)

References 35 publications

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“…RAFF-Net could adapt well to the interference of different tongue shapes and differences in tooth mark details. Compared with UNet and other tongue segmentation model, 9,15,17,42,43 our model RAFF-Net improved MIoU by 1.54%, 2.63%, 0.70%, 6.18%, 1.70%. The comparison between the improved algorithm RAFF-Net and OETNet 9 in this article revealed that RAFF-Net had fewer numbers of parameters, and the computing time of both was basically comparable.…”

Section: Resultsmentioning

confidence: 87%

“…The performance of the latest improved models related to tongue segmentation was also compared, involving several tongue segmentation models mentioned in the literature. 9,15,17,42,43 To ensure the objectivity of the model performance judging metrics, seven metrics, MIoU, F1-score, Recall, Precision, number of parameters, MFLOPs, and Runtime, were chosen for a comprehensive comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Besides, Jiang et al 16 designed a tongue image segmentation method using an enhanced Hue, Saturation, Value (HSV) color model convolutional neural network. Huang et al 17 employed a deep residual network as an encoder, a feature pyramid network as a decoder, and fused multiscale feature maps to achieve better tongue segmentation. Zhang et al 18 combined Atrus convolutional algorithm, Atrus spatial pyramid pooling module and fully connected conditional random field to refine the edges of segmented tongue images.…”

Section: Introductionmentioning

confidence: 99%

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

Song

Huang

et al. 2022

DIGITAL HEALTH

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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.

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Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Song

Huang

et al. 2022

DIGITAL HEALTH

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“…In the last two years, tongue image segmentation algorithms have been developed based on traditional tongue image segmentation methods and deep learning algorithms. To solve the problems of edge blurring and detail interference during tongue extraction, Huang et al 112 designed an automatic tongue image segmentation method using an enhanced full convolutional network with an encoderdecoder structure with an average sensitivity of 98.97%, which is better than the four algorithms SegNet, FCN, PSPNet, and DeepLab v3 + . Gao et al 113 proposed LSM-SEC based on convolutional neural networks as a model combining symmetric and edge-constrained level sets of tongue geometric features for tongue segmentation, which is suitable for tongue image segmentation under most conditions and can also improve the accuracy of subsequent model evolution.…”

Section: Deep-learning-based Tongue Segmentation Methodsmentioning

confidence: 99%

Research and application of tongue and face diagnosis based on deep learning

Huang

Zhong

et al. 2022

DIGITAL HEALTH

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Objective To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. Methods Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application. Results Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms. Conclusion There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.

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“…The method based on deep learning can acquire more image features and has better performance. Huang et al [ 21 ] proposed an automatic tongue image segmentation based on an enhanced full convolutional network. Qu et al [ 22 ] proposed an image quality evaluation method based on brightness statistics to determine whether the input image needs to be segmented and used SegNet to train the tongue dataset.…”

Section: Introductionmentioning

confidence: 99%

LSM‐SEC: Tongue Segmentation by the Level Set Model with Symmetry and Edge Constraints

Gao

Guo

Mao

2021

Computational Intelligence and Neuroscience

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Accurate segmentation of the tongue body is an important prerequisite for computer-aided tongue diagnosis. In general, the size and shape of the tongue are very different, the color of the tongue is similar to the surrounding tissue, the edge of the tongue is fuzzy, and some of the tongue is interfered by pathological details. The existing segmentation methods are often not ideal for tongue image processing. To solve these problems, this paper proposes a symmetry and edge-constrained level set model combined with the geometric features of the tongue for tongue segmentation. Based on the symmetry geometry of the tongue, a novel level set initialization method is proposed to improve the accuracy of subsequent model evolution. In order to increase the evolution force of the energy function, symmetry detection constraints are added to the evolution model. Combined with the latest convolution neural network, the edge probability input of the tongue image is obtained to guide the evolution of the edge stop function, so as to achieve accurate and automatic tongue segmentation. The experimental results show that the input tongue image is not subject to the external capturing facility or environment, and it is suitable for tongue segmentation under most realistic conditions. Qualitative and quantitative comparisons show that the proposed method is superior to the other methods in terms of robustness and accuracy.

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TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

Cited by 18 publications

References 35 publications

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

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

Research and application of tongue and face diagnosis based on deep learning

LSM‐SEC: Tongue Segmentation by the Level Set Model with Symmetry and Edge Constraints

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