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%.

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

Potential use of a dried saliva spot(DSS)in therapeutic drug monitoring and disease diagnosis

In recent years,scientific researchers have increasingly become interested in noninvasive sampling methods for therapeutic drug monitoring and disease diagnosis.As a result,dried saliva spot(DSS),which is a sampling technique for collecting dried saliva samples,has been widely used as an alternative matrix to serum for the detection of target molecules.Coupling the DSS method with a highly sensitive detection instrument improves the efficiency of the preparation and analysis of biological samples.Furthermore,dried blood spots,dried plasma spots,and dried matrix spots,which are similar to those of the DSS method,are discussed.Compared with alternative biological fluids used in dried spot methods,including serum,tears,urine,and plasma,saliva has the advantage of convenience in terms of sample collection from children or persons with disabilities.This review aims to provide integral strategies and guidelines for dried spot methods to analyze biological samples by illustrating several dried spot methods.Herein,we summarize recent advancements in DSS methods from June 2014 to March 2021 and discuss the advantages and disadvantages of the key aspects of this method,including sample preparation and method validation.Finally,we outline the challenges and prospects of such methods in practical applications.

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.

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository.The experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process.Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions.Since the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research communities.This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)model.The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter tuning.Besides,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection technique.Moreover,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis disease.Furthermore,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter values.In order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical datasets.The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.

Based On K-means Disease Diagnosis Research

For the diagnosis of diseases,modern medicine usually searches for diseases in the disease database to find the type of disease that matches them.The diagnosis of diseases is the first step in treatment.Then the classification of diseases is the basis of disease diagnosis.Disease classification plays an extremely important role in the scientific management of medical records and the development of modern medicine,and is a bridge connecting modern medical science.Therefore,the classification of diseases is very necessary.Based on this,this article establishes a K-means model for disease diagnosis,and combines the internationally unified disease type code ICD statistics table to classify the sample data set into infectious and parasitic diseases,tumors,diabetes and circulatory diseases The training is perfect,and finally the diagnosis classification of the disease is realized.

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.

Analysis of Non-destructive Testing and Disease Diagnosis Technology of Tunnel Projects

Engineering incidents caused by the quality of tunnel construction and geological diseases occur from time to time,which not only causes many problems in engineering geophysical prospecting,but also provided a broad space for the application and development of engineering geophysical prospecting technology.Non-destructive testing technology has made great progress.Combining the diagnosis and treatment of tunnel diseases,the ground penetrating radar non-destructive detection technology is discussed.

Weighted Heterogeneous Graph-Based Incremental Automatic Disease Diagnosis Method

The objective of this study is to construct a multi-department symptom-based automatic diagnosis model.However,it is dificult to establish a model to classify plenty of diseases and collect thousands of disease-symptom datasets simultaneously.Inspired by the thought of"knowledge graph is model",this study proposes to build an experience-infused knowledge model by continuously learning the experiential knowledge from data,and incrementally injecting it into the knowledge graph.Therefore,incremental learning and injection are used to solve the data collection problem,and the knowledge graph is modeled and containerized to solve the large-scale multi-classification problems.First,an entity linking method is designed and a heterogeneous knowledge graph is constructed by graph fusion.Then,an adaptive neural network model is constructed for each dataset,and the data is used for statistical initialization and model training.Finally,the weights and biases of the learned neural network model are updated to the knowledge graph.It is worth noting that for the incremental process,we consider both the data and class increments.We evaluate the diagnostic effectiveness of the model on the current dataset and the anti-forgetting ability on the historical dataset after class increment on three public datasets.Compared with the classical model,the proposed model improves the diagnostic accuracy of the three datasets by 5%,2%,and 15%on average,respectively.Meanwhile,the model under incremental learning has a better ability to resist forgetting.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Advances of nanoparticles as drug delivery systems for disease diagnosis and treatment

Decades have passed since the first nanoparticles-base medicine was approved for human cancer treatment, and the research and development of nanoparticles for drug delivery are always undergoing.Nowadays, the significant advances complicate nanoparticles’ branches, including liposomes, solid lipid nanoparticles, inorganic nanoparticles, micelles, nanovaccines and nano-antibodies, etc. These nanoparticles show numerous capabilities in treatment and diagnosis of stubborn diseases like cancer and neurodegenerative diseases, emerging as novel drug carriers or therapeutic agents in future. In this review, the complicated branches of nanoparticles are classified and summarized, with their property and functions concluded. Besides, there are also some delivery strategies that make nanoparticles smarter and more efficient in drug delivery, and frontiers in these strategies are also summarized in this review. Except these excellent works in newly-produced drug delivery nanoparticles, some points of view and future expectations are made in the end.

The application of CRISPR-Cas in disease diagnosis and treatment

Clustered regularly interspaced short palindromic repeat(CRISPR)has been gaining much attention in the modern medical field and has been widely used for the diagnosis and treatment of diseases in recent years.In this review,we will introduce the application of CRISPR in disease diagnosis and treatment,including its use in detecting pathogens,gene mutations,and genetic diseases,as well as its application in gene therapy for single-gene diseases,cancer,viral infectious diseases,and cardiovascular diseases.Additionally,we will discuss the potential future directions and challenges of CRISPR in the diagnosis and treatment of diseases,and provide a thorough overview of the ways in which CRISPR is used for diagnosing and treating diseases.

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus

AIM:To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages,limitations,and possible solutions common to all tasks.METHODS:We searched three academic databases,including PubMed,Web of Science,and Ovid,with the date of August 2022.We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords,of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images.RESULTS:Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance,including diabetic retinopathy,glaucoma,age-related macular degeneration,retinal vein occlusions,retinal detachment,and other peripheral retinal diseases.Compared to fundus images,the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200°in a single exposure,which can observe more areas of the retina.CONCLUSION:The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

In vivo Self-assembled Peptide Nanoprobes for Disease Diagnosis

In vivo imaging is creating great opportunities for disease diagnosis as a research tool. Probes are usually used to observe physiological structures in vivo clearly. Recent progresses of nanoprobes are important for the generation of high resolution and high contrast images required by accurate and precision disease diagnosis. In vivo self-assembled peptide(SAP) nanoprobes are playing major roles in in vivo imaging by modularity of design, high imaging contrast, response to the location of the lesion, and long-time retention in the lesion. And the response to lesion and long-term retention in there can enhance imaging sensitivity and specificity of in vivo SAP nanoprobes. Therefore, in vivo SAP nanoprobes are simple ancillary contrast entities to optimize the imaging effect. In this review, the recent progress of in vivo SAP nanoprobes for in vivo imaging, from molecular design of peptides to biomedical and clinical applications including disease diagnosis and disease-related molecular imaging is systematically summarized. We evaluate their ability, including sensitivity and specificity to provide relevant information under preoperative and during surgery circumstances and critically their likelihood to be clinically translated. Finally, a brief outlook on remaining challenges and potential directions for future research in this area is presented.

A Classifier Based on Rough Set and Relevance Vector Machine for Disease Diagnosis

A new intelligent method for disease diagnosis based on rough set theory （RST） and the relevance vector machine （RVM） for classification is presented as the rough relevance vector machine （RRVM）. The RRVM mixes rough set＇s strong rule extraction ability with the excellent classification ability of the relevance vector machine through preprocessing initial information, reducing data, and training the relevance vector machine. Compared with traditional intelligence methods such as neural network（NN）, support vector machine（SVM）, and relevance vector machine （RVM）, this method manages to identify disease samples objectively and effectively with less transcendental information.

Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.