Tongue image feature correlation analysis in benign lung nodules and lung cancer

Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.Methods From July 1;2020 to March 31;2022;clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hos-pital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Ex-amination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chi-nese Medicine;respectively.We obtained tongue images from patients with benign lung nod-ules and lung cancer using the TFDA-1 digital tongue diagnosis instrument;and analyzed these images with the TDAS V2.0 software.The extracted indicators included color space pa-rameters in the Lab system for both the tongue body(TB)and tongue coating(TC)(TB/TC-L;TB/TC-a;and TB/TC-b);textural parameters[TB/TC-contrast(CON);TB/TC-angular second moment(ASM);TB/TC-entropy(ENT);and TB/TC-MEAN];as well as TC parameters(perAll and perPart).The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis;and the overall correlation was analyzed using canonical correlation analysis(CCA).Results Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values.Simple correlation analysis indi-cated that the correlation of TB-L with TC-L;TB-b with TC-b;and TB-b with perAll in lung cancer group was higher than that in benign nodules group.Moreover;the correlation of TB-a with TC-a;TB-a with perAll;and the texture parameters of the TB(TB-CON;TB-ASM;TB-ENT;and TB-MEAN)with the texture parameters of the TC(TC-CON;TC-ASM;TC-ENT;and TC-MEAN)in benign nodules group was higher than lung cancer group.CCA further demon-strated a strong correlation between the TB and TC parameters in lung cancer group;with the first and second pairs of typical variables in benign nodules and lung cancer groups indicat-ing correlation coefficients of 0.918 and 0.817(P<0.05);and 0.940 and 0.822(P<0.05);re-spectively.Conclusion Benign lung nodules and lung cancer patients exhibited differences in correla-tion in the L;a;and b values of the TB and TC;as well as the perAll value of the TC;and the texture parameters(TB/TC-CON;TB/TC-ASM;TB/TC-ENT;and TB/TC-MEAN)between the TB and TC.Additionally;there were differences in the overall correlation of the TB and TC be-tween the two groups.Objective tongue diagnosis indicators can effectively assist in the diag-nosis of benign lung nodules and lung cancer;thereby providing a scientific basis for the ear-ly detection;diagnosis;and treatment of lung cancer.

Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine,for example,traditional Chinese medicine(TCM),Japanese traditional herbal medicine,and traditional Korean medicine(TKM).The diagnosis procedure is mainly based on the expert’s knowledge depending upon the visual inspec-tion comprising color,substance,coating,form,and motion of the tongue.But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective.Therefore,computer-aided tongue analyses have a greater potential to present objective and more consistent health assess-ments.This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis(SADTL-TIADD)model.The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality.Next,the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors.In turn,the SA with the ELM model enhances classification efficiency for disease detection and classification.The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work.A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm.The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

Tongue image segmentation and tongue color classification based on deep learning

Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.

Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis.Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously.Traditionally,physicians examine the characteristics of tongue prior to decision-making.In this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed.This model can reduce the physical harm made to the patients.Several tongue image analytical methodologies have been proposed earlier.However,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis model.With this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI model.The proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal process.Besides,SqueezeNet model is employed as a feature extractor in which the hyperparameters of SqueezeNet are tuned using Oppositional Glowworm Swarm Optimization(OGSO)algorithm.At last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color images.The design of OGSO algorithm for SqueezeNet model shows the novelty of the work.To assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several measures.The resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images

Objective:To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease(NAFLD) based on features of tongue images.Methods:Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019,then the anthropometric indexes and sampled tongue images were measured.The tongue images were labeled by features,based on a brief protocol,without knowing any other clinical data,after a series of corrections and data cleaning.The algorithm was trained on images using labels and several anthropometric indexes for inputs,utilizing machine learning technology.Finally,a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD.Results:A total of 720 subjects were enrolled in this study,including 432 patients with NAFLD and 288 healthy volunteers.Of them,482 were randomly allocated into the training set and 238 into the validation set.The diagnostic model based on logistic regression exhibited excellent performance:in validation set,it achieved an accuracy of 86.98%,sensitivity of 91.43%,and specificity of 80.61%;with an area under the curve(AUC) of 0.93 [95% confidence interval(CI) 0.68–0.98].The decision tree model achieved an accuracy of 81.09%,sensitivity of 91.43%,and specificity of 66.33%;with an AUC of 0.89(95% CI 0.66–0.92) in validation set.Conclusions:The features of tongue images were associated with NAFLD.Both the 2 diagnostic models,which would be convenient,noninvasive,lightweight,rapid,and inexpensive technical references for early screening,can accurately distinguish NAFLD and are worth further study.

Development and validation of tongue imaging-based radiomics tool for the diagnosis of insomnia degree:a two-center study

Background:Traditional Chinese medicine(TCM)is commonly used for the diagnosis and treatment of insomnia,with tongue diagnosis being particularly important.The aim of our study was to develop and validate a novel tongue imaging-based radiomics(TIR)method for accurately diagnosing insomnia severity.Methods:This two-center analysis prospectively enrolled 399 patients who underwent tongue imaging between July and October 2021 and divided them into primary and validation cohorts by study center.Here,we referred to the Insomnia Severity Index(ISI)standard and the degree of insomnia was evaluated as absent,subthreshold,moderate,or severe.For developed the TIR diagnostic tool,a U-Net algorithm was used to segment tongue images.Subsequently,seven imaging features were selected from the extracted high-throughout radiomics features using the least absolute shrinkage and selection operator algorithm.Then,the final radiomics model was developed in the primary cohort and tested in the independent validation cohort.Finally,we assessed and compared the diagnostic performance differences between TCM tongue diagnosis and our TIR diagnostic tool with the ISI gold standard.The confusion matrix was calculated to evaluate the diagnostic performance.Results:Seven tongue imaging features were selected to build the TIR tool,with showing good correlations with the insomnia degree.The TIR method had an accuracy of 0.798,a macro-average sensitivity of 0.78,a macro-average specificity of 0.906,a weighted-average sensitivity of 0.798,and a weighted specificity of 0.916,showing a significantly better performance compared to the average performance of three experienced TCM physicians(mean accuracy of 0.458,P<0.01).Conclusions:The preliminary study demonstrates the potential application of TIR in the diagnosis of insomnia degree and measurement of sleep health.The integration of quantitative imaging analysis and machine learning algorithms holds promise for advancing both of TCM and precision sleep medicine.

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

Research on Tongue Image Collection and Analysis Based on Smartphone

Tongue diagnosis is a diagnostic method to understand the physiological functions and pathological changes of the human body by observing human tongue quality,tongue coating,and sublingual collateral veins.Tongue elephant research is an important part of objective Chinese medicine.Based on the research of tongue image analysis method,the design of a tongue image collection analysis system based on the smartphone platform was demonstrated.This research uses a smartphone camera to collect tongue images,upload them to the server and complete tongue image analysis on the server.Finally,the tongue image analysis results are generated,and the results are transmitted from the server to the smartphone mobile terminal.We used HTTP protocol for communication between mobile client and server.We asked professional TCM doctors to evaluate the results of tongue analysis in 100 cases,and the final anastomosis rate was above 80%.This study fills in the gaps in the mobile platform of objectification of tongue diagnosis,which is conducive to the use of mobile phones to collect tongue images anytime,anywhere,and to conveniently share the results of tongue diagnosis with physicians.With an Internet connection,users can use their smartphones to remotely diagnose their tongues,who can store and generate their own personal diagnostic reports.

Fusion Based Tongue Color Image Analysis Model for Biomedical Applications

Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe.Recently,several deep learning(DL)based tongue color image analysis models have existed in the literature for the effective detection of diseases.This paper presents a fusion of handcrafted with deep features based tongue color image analysis(FHDF-TCIA)technique to biomedical applications.The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model,and thereby determines the existence of disease.Primarily,the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise.The proposed FHDF-TCIA model encompasses a fusion of handcrafted local binary patterns(LBP)withMobileNet based deep features for the generation of optimal feature vectors.In addition,the political optimizer based quantum neural network(PO-QNN)based classification technique has been utilized for determining the proper class labels for it.A detailed simulation outcomes analysis of the FHDF-TCIA technique reported the higher accuracy of 0.992.

Application of artificial intelligence in tongue diagnosis of traditional Chinese medicine:A review

Tongue diagnosis is an important process to non-invasively assess the condition of a patient’s internal organs in traditional Chinese medicine(TCM)and each part of the tongue is related to corresponding internal organs.Due to continuing computer technological advances,especially the artificial intelligence(AI)methods have achieved significant success in tackling tongue image acquisition,processing,and classification,novel AI methods are being introduced in traditional Chinese medicine tongue diagnosis medical practices.Traditional tongue diagnose depends on observations of tongue characteristics,such as color,shape,texture,moisture,etc.by traditional Chinese medicine physicians.The appearance of the tongue color,texture and coating reflects the improvement or deterioration of patient’s conditions.Moreover,AI can now distinguish patient’s condition through tongue images,texture or coating,which is all possible increasingly with help from traditional Chinese medicine physicians under the traditional Chinese medicine tongue theory.AI has enabled humans to do what was previously unimagined:traditional Chinese medicine tongue diagnosis with feeding a large amount of tongue image and tongue texture/coating data to train the AI modes.This review focuses on the research advances of AI in TCM tongue diagnosis thus far to identify the major scientific methods and prospects.In this article,we tried to review the AI application in resolving the tongue diagnosis of traditional Chinese medicine on color correction,tongue image extraction,tongue texture/coating segmentation.

Assessment of traditional Chinese medicine pattern in a bleomycininduced pulmonary fibrosis mouse model: A pilot study

Objective:To initially explore traditional Chinese medicine patterns in a bleomycin-induced pulmonary fibrosis mouse model.Methods:Thirty-six C57BL/6 mice were divided by the random number table method(with 12 rats per group)into three groups:a blank group,a model group,and a number 2 Feibi recipe(FBR-2)group.The pulmonary fibrosis mouse model was established by intratracheal instillation of bleomycin.The FBR-2 group was treated with FBR-2 for 4 weeks.Symptoms in the mice such as mental behavior,food/water intake,body weight,body temperature,respiratory rate,and tongue image were observed.The samples were collected on the 14th day and 28th day after modeling,and lung tissues were visually assessed and microscopically evaluated by staining with hematoxylin-eosin and Masson.The expression levels of hydroxyproline,interleukin(IL)-33,IL-37,tissue plasminogen activator,and plasminogen activator inhibitor-1 were determined by enzyme-linked immunosorbent assay.Results:Mice in the model group were poor in spirit,less active,slow in response,showed reduced food/water intake,body temperature,and body weight,increased respiratory rate,and their tongue color had changed from light red to dark red.However,treatment with FBR-2 significantly improved these symptoms.Extensive inflammatory cell infiltration and collagen fiber deposition were observed in the lung tissues of the model group.Compared with the blank group,the levels of hydroxyproline,IL-33,and plasminogen activator inhibitor-1 in the model group significantly increased(all P<.05),whereas that of tissue plasminogen activator significantly decreased on the 14th day and 28th day(P=.036 and P=.005,respectively).Moreover,FBR-2 improved lung inflammation and fibrinolysis imbalance and reduced collagen fiber deposition.Conclusion:To some extent,our bleomycin-induced pulmonary fibrosis mouse model exhibited traditional Chinese medicine patterns of qi deficiency,blood stasis,and heat retention.

Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model

Objective:To develop a multimodal deep-learning model for classifying Chinese medicine constitution,i.e.,the balanced and unbalanced constitutions,based on inspection of tongue and face images,pulse waves from palpation,and health information from a total of 540 subjects.Methods:This study data consisted of tongue and face images,pulse waves obtained by palpation,and health information,including personal information,life habits,medical history,and current symptoms,from 540 subjects(202 males and 338 females).Convolutional neural networks,recurrent neural networks,and fully connected neural networks were used to extract deep features from the data.Feature fusion and decision fusion models were constructed for the multimodal data.Results:The optimal models for tongue and face images,pulse waves and health information were ResNet18,Gate Recurrent Unit,and entity embedding,respectively.Feature fusion was superior to decision fusion.The multimodal analysis revealed that multimodal data compensated for the loss of information from a single mode,resulting in improved classification performance.Conclusions:Multimodal data fusion can supplement single model information and improve classification performance.Our research underscores the effectiveness of multimodal deep learning technology to identify body constitution for modernizing and improving the intelligent application of Chinese medicine.