TongueNet: A Precise and Fast Tongue Segmentation System Using U-Net with a Morphological Processing Layer

Zhou, Jianhang; Zhang, Qi; Zhang, Bob; Chen, Xiaojiao

doi:10.3390/app9153128

Cited by 43 publications

(29 citation statements)

References 24 publications

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“…Thus, there are three main advantages of applying U-Net for medical imaging segmentation. The U-Net is robust and sturdy in small-size datasets; it is an effective and efficient method in medical image segmentation, can obtain the ideal performance; its structure is concise and easy to reform for further improvement [29]. In Fig.…”

Section: A the First Sub-network Architecturementioning

confidence: 99%

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

et al. 2020

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Section: A the First Sub-network Architecturementioning

confidence: 99%

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

et al. 2020

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“…Zhou et al [19] presented Tongue Net that is a specific and faster automated tongue segmentation scheme. The U-net is used as segmentation backbone employing a smaller scale image dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

2021

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In recent times, internet of things (IoT) and wireless communication techniques become widely used in healthcare sector. Biomedical image processing is commonly employed to detect the existence of diseases using biomedical images. Tongue diagnosis is an efficient, non-invasive model to perform auxiliary diagnosis any time anywhere that is support the global necessity in the primary healthcare system. Conventionally, medical practitioners investigate the tongue features based on their expert's knowledge comes from experience. In order to eradicate the qualitative aspects, tongue images can be quantitatively examined, offering an effective disease diagnostic process in such a way that the physical harm of the patients can be minimized. Numerous tongue image analysis approaches exist in the literature, it is required to develop automated deep learning (DL) models to diagnose the diseases using tongue image analysis. In this view, this paper designs an automated IoT and synergic deep learning based tongue color image (ASDL-TCI) analysis model for disease diagnosis and classification. The proposed ASDL-TCI model operates on major stages namely data acquisition, pre-processing, feature extraction, classification, and parameter optimization. Primarily, the IoT devices are used to capture the human tongue images and transmitted to the cloud for further analysis. In addition, median filtering based image pre-processing and SDL based feature extraction techniques are employed. Moreover, deep neural network (DNN) based classifier is applied to determine the existence of the diseases. Lastly, enhanced black widow optimization (EBWO) based parameter tuning process takes place to enhance the diagnostic performance. For assessing the effectual performance of the ASDL-TCI model, a set of simulations take place on benchmark tongue images and examined the results under distinct dimensions. The simulation outcome verified the enhanced diagnostic performance of the ASDL-TCI model over the compared methods with the maximum precision, recall, and accuracy of 0.984, 0.973, and 0.983. INDEX TERMSBiomedical image processing, Tongue color image analysis, Deep learning, Deep neural network, Parameter tuning, Synergic Deep Learning, Internet of Things.

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“…On the other hand, large‐scale annotated data are expensive to obtain for a highly specific medical imaging domain such as tongue segmentation. In light of this, Zhou et al 38 propose an integrated framework for tongue segmentation, which adopts U‐net for its initial segmentation and further exploits image morphology to fill the gaps between the tongue body and the background. In addition, by using a data sampling strategy, an improved CNN with a ResNet backbone is proposed in Reference 39.…”

Section: Related Workmentioning

confidence: 99%

A robust interclass and intraclass loss function for deep learning based tongue segmentation

Cai

Wang

Liu

et al. 2020

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The fast and robust segmentation of tongue images is a prerequisite to achieve automatic tongue diagnosis in traditional Chinese medicine. In order to assist tongue diagnosis in real-life scenarios, an ideal tongue segmentation method would need to obtain the entire tongue body as well as its precise contours. However, the similar appearance among the tongue body, the coating, and the lips hinders the performance for most unsupervised learning methods that primarily utilize low-level visual features. On the other hand, although the supervised deep convolutional neural networks (DCNNs) that typically depend on the widely used crossentropy loss can achieve better accuracy, they are very prone to segment image to multiple trivially areas. To address both of the above issues, we make an attempt to boost the segmentation performance of DCNNs with a novel auxiliary loss function that seeks to exploit large margin learning for end-to-end tongue segmentation models. Specifically, we first propose a loss function that involves interclass and intraclass costs to directly measure the distance among pixels that belong to different connected regions. Then, we explore the potential ability of this loss function as a regularization for different segmentation networks such as those with attention modules and the deeply supervised network. Finally, a theoretical analysis for this learning scheme is presented. Through experiments on challenging datasets, we show that the proposed approach can be easily integrated into state-of-the-art networks to boost their performance at the tongue segmentation task without bells and whistles.

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TongueNet: A Precise and Fast Tongue Segmentation System Using U-Net with a Morphological Processing Layer

Cited by 43 publications

References 24 publications

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

Automatic Epicardial Fat Segmentation and Quantification of CT Scans Using Dual U-Nets With a Morphological Processing Layer

Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

A robust interclass and intraclass loss function for deep learning based tongue segmentation

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