EDSUCh:A robust ensemble data summarization method for effective medical diagnosis

Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data.Data summarization can create a concise version of the original data that can be used for effective diagnosis.In this paper,we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns.To the best of our knowledge,there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis.The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used.Therefore,the medical diagnosis becomes more effective,and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.

Medical Diagnosis Using Machine Learning:A Statistical Review

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate diagnosis.Machine learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis.Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published.Hence,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this study.To carry out the review,six different databases are selected.Inclusion and exclusion criteria are employed to limit the research.Further,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and results.Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis.The ndings of this study show the most used ML methods and the most common diseases that are focused on by researchers.It also shows the increase in use of machine learning for disease diagnosis over the years.These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.

A consistency contribution based bayesian network model for medical diagnosis

This paper presents an effective Bayesian network model for medical diagnosis. The proposed approach consists of two stages. In the first stage, a novel feature selection algorithm with consideration of feature interaction is used to get an undirected network to construct the skeleton of BN as small as possible. In the second stage for greedy search, several methods are integrated together to enhance searching performance by either pruning search space or overcoming the optima of search algorithm. In the experiments, six disease datasets from UCI machine learning database were chosen and six off-the-shelf classification algorithms were used for comparison. The result showed that the proposed approach has better classification accuracy and AUC. The proposed method was also applied in a real world case for hypertension prediction. And it presented good capability of finding high risk factors for hypertension, which is useful for the prevention and treatment of hypertension. Compared with other methods, the proposed method has the better performance.

Medical Diagnosis Expert System for Malaria and Related Diseases for Developing Countries

There is a strong need for cost-effective technologies to manage disease processes and thus reduce morbidity and mortality in the developing countries. Yet bringing intelligent healthcare informatics to bear on the dual problems of reducing healthcare costs and improving quality and outcomes is a challenge even in countries with a reasonably developed technology infrastructure. This paper focused at how appropriate an ap-plication of Medical Diagnosis Expert System (MDES) is to manage diseases in developing countries. MDES is usually designed to enable clinicians to identify diseases and describe methods of treatment to be carried out taking into account the user capability. The MDES described here is implemented using the C Language Integrated Production System (CLIPS). The CLIPS is an expert system, which has a shell composed of four modules: the user interface, the explanation system, the inference engine and the knowledge base editor. In the system, a number of patient cases will be selected as prototypes and stored in a separate database. The knowledge is acquired from literature review, human experts and the internet of the specific domain and is used as a base for analysis, diagnosis and recommendations.

Clinical diagnosis,treatment,and medical identification of specific pulmonary infection in naval pilots:Four case reports

BACKGROUND Specific pulmonary infection could seriously threaten the health of pilots and their companions.The consequences are serious.We investigated the clinical diagnosis,treatment,and medical identification of specific pulmonary infections in naval pilots.CASE SUMMARY We analyzed the medical waiver and clinical data of four pilots with specific pulmonary infections,who had accepted treatment at the Naval Medical Center of Chinese People’s Liberation Army between January 2020 and November 2021,including three cases of tuberculosis and one of cryptococcal pneumonia.All cases underwent a series of comprehensive treatment courses.Three cases successfully obtained medical waiver for flight after being cured,while one was grounded after reaching the maximum flight life after being cured.CONCLUSION Chest computed tomography scanning should be used instead of chest radiography in pilots’physical examination.Most pilots with specific pulmonary infection can be cured and return to flight.

Intuitionistic Neuro-Fuzzy Optimization in the Management of Medical Diagnosis

Diabetes has become a major concern nowadays and its complications are affecting various organs of a diabetic patient. Therefore, a multi-dimensional technique including all parameters is required to detect the cause, its proper diagnostic procedure and its prevention. In this present work, a technique has been introduced that seeks to build an implementation for the intelligence system based on neural networks. Moreover, it has been described that how the proposed technique can be used to determine the membership together with the non-membership functions in the intuitionistic environment. The dataset has been obtained from Pima Indians Diabetes Database (PIDD). In this work, a complete diagnostic procedure of diabetes has been introduced with seven layered structural frameworks of an Intuitionistic Neuro Sugeno Fuzzy System (INSFS). The first layer is the input, in which six factors have been taken as an input variable. Subsequently, a neural network framework has been developed by constructing IFN for all the six input variables, and then this input has been fuzzified by using triangular intuitionistic fuzzy numbers. In this work, we have introduced a novel optimization technique for the parameters involved in the INSFS. Moreover, an inference system has also been framed for the neural network known as INFS. The results have also been given in the form of tables, which describe each concluding factor.

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy.Deep learning provides a high performance for several medical image analysis applications.This paper proposes a deep learning model for the medical image fusion process.This model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR images.Then,an additional process is executed on the extracted features.After that,the fused feature map is reconstructed to obtain the resulting fused image.Finally,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality.Different realistic datasets of different modalities and diseases are tested and implemented.Also,real datasets are tested in the simulation analysis.

Review of intelligent diagnosis methods for imaging gland cancer based on machine learning

Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s knowledge.In this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class labels.In order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms

Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)models.The conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical images.The recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification problems.But the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation cost.In this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image classification.The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images.In addition,region growing segmentation technique is involved to determine the infected regions in the medical image.Moreover,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature vectors.Furthermore,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification process.In order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 datasets.The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures.Please type your abstract here.

Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

Medical image analysis is an active research topic,with thousands of studies published in the past few years.Transfer learning(TL)including convolutional neural networks(CNNs)focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance.It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time.This study develops an EnhancedTunicate SwarmOptimization withTransfer Learning EnabledMedical Image Analysis System(ETSOTL-MIAS).The goal of the ETSOTL-MIAS technique lies in the identification and classification of diseases through medical imaging.The ETSOTL-MIAS technique involves the Chan Vese segmentation technique to identify the affected regions in the medical image.For feature extraction purposes,the ETSOTL-MIAS technique designs a modified DarkNet-53 model.To avoid the manual hyperparameter adjustment process,the ETSOTLMIAS technique exploits the ETSO algorithm,showing the novelty of the work.Finally,the classification of medical images takes place by random forest(RF)classifier.The performance validation of the ETSOTL-MIAS technique is tested on a benchmark medical image database.The extensive experimental analysis showed the promising performance of the ETSOTL-MIAS technique under different measures.

Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model

Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and skin cancer.Most traditional CAD methods relied on textures,colours,and forms.Because many models are issue-oriented,they need a more substantial capacity to generalize and cannot capture high-level problem domain notions.Recent deep learning(DL)models have been published,providing a practical way to develop models specifically for classifying input medical pictures.This paper offers an intelligent beetle antenna search(IBAS-DTL)method for classifying medical images facilitated by deep transfer learning.The IBAS-DTL model aims to recognize and classify medical pictures into various groups.In order to segment medical pictures,the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment(EWFCM)approach.Additionally,the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine(ELM)model is used to classify the medical photos.A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model’s noteworthy performance.The results gained indicated the IBAS-DTL model’s superiority over its pre-existing techniques.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

An Automatic Implementation of Oropharyngeal Swab Sampling for Diagnosing Respiratory Infectious Diseases via Soft Robotic End-Effectors

The most widely adopted method for diagnosing respiratory infectious diseases is to conduct polymerase chain reaction(PCR)assays on patients’respiratory specimens,which are collected through either nasal or oropharyngeal swabs.The manual swab sampling process poses a high risk to the examiner and may cause false-negative results owing to improper sampling.In this paper,we propose a pneumatically actuated soft end-effector specifically designed to achieve all of the tasks involved in swab sampling.The soft end-effector utilizes circumferential instability to ensure grasping stability,and exhibits several key properties,including high load-to-weight ratio,error tolerance,and variable swab-tip stiffness,leading to successful automatic robotic oropharyngeal swab sampling,from loosening and tightening the transport medium tube cap,holding the swab,and conducting sampling,to snapping off the swab tail and sterilizing itself.Using an industrial collaborative robotic arm,we integrated the soft end-effector,force sensor,camera,lights,and remote-control stick,and developed a robotic oropharyngeal swab sampling system.Using this swab sampling system,we conducted oropharyngeal swab-sampling tests on 20 volunteers.Our Digital PCR assay results(RNase P RNA gene absolute copy numbers for the samples)revealed that our system successfully collected sufficient numbers of cells from the pharyngeal wall for respiratory disease diagnosis.In summary,we have developed a pharyngeal swab-sampling system based on an“enveloping”soft actuator,studied the sampling process,and imple-mented whole-process robotic oropharyngeal swab-sampling.

Medical expenditure for esophageal cancer in China:a 10-year multicenter retrospective survey(2002-2011)

Background: Esophageal cancer is associated with substantial disease burden in China, and data on the economic burden are fundamental for setting priorities in cancer interventions. The medical expenditure for the diagnosis and treatment of esophageal cancer in China has not been fully quantified. This study aimed to examine the medical expenditure of Chinese patients with esophageal cancer and the associated trends.Methods: From 2012 to 2014, a hospital-based multicenter retrospective survey was conducted in 37 hospitals in 13 provinces/municipalities across China as a part of the Cancer Screening Program of Urban China. For each esophageal cancer patient diagnosed between 2002 and 2011, clinical information and expense data were extracted by using structured questionnaires. All expense data were reported in Chinese Yuan(CNY; 1 CNY = 0.155 USD) based on the2011 value and inflated using the year-specific health care consumer price index for China.Results: A total of 14,967 esophageal cancer patients were included in the analysis. It was estimated that the overall average expenditure per patient was 38,666 CNY, and an average annual increase of 6.27% was observed from 2002(25,111 CNY) to 2011(46,124 CNY). The average expenditures were 34,460 CNY for stage Ⅰ,39,302 CNY for stage Ⅱ,40,353 CNY for stage Ⅲ, and 37,432 CNY for stage IV diseases(P < 0.01). The expenditure also differed by the therapy type, which was 38,492 CNY for surgery, 27,933 CNY for radiotherapy, and 27,805 CNY for chemotherapy(P < 0.05).Drugs contributed to 45.02% of the overall expenditure.Conclusions: These conservative estimates suggested that medical expenditures for esophageal cancer in China substantially increased in the last 10 years, treatment for early-stage esophageal cancer costs less than that for advanced cases, and spending on drugs continued to account for a considerable proportion of the overall expenditure.

Diagnostic value and safety of medical thoracoscopy for pleural effusion of different causes

BACKGROUND Pleural effusions occur for various reasons,and their diagnosis remains challenging despite the availability of different diagnostic modalities.Medical thoracoscopy(MT)can be used for both diagnostic and therapeutic purposes,especially in patients with undiagnosed pleural effusion.AIM To assess the diagnostic efficacy and safety of MT in patients with pleural effusion of different causes.METHODS Between January 1,2012 and April 30,2021,patients with pleural effusion underwent MT in the Department of Respiratory Medicine,The Second Affiliated Hospital of Xi’an Jiaotong University(Shaanxi,China).According to the discharge diagnosis,patients were divided into malignant pleural effusion(MPE),tuberculous pleural effusion(TBPE),and inflammatory pleural effusion(IPE)groups.General information,and tuberculosis-and effusion-related indices of the three groups were analyzed.The diagnostic yield,diagnostic accuracy,performance under thoracoscopy,and complications of patients were compared among the three groups.Then,the significant predictive factors for diagnosis between the MPE and TBPE groups were analyzed.RESULTS Of the 106 patients enrolled in this 10-year study,67 were male and 39 female,with mean age of 57.1±14.184 years.Among the 74 thoracoscopy-confirmed patients,41(38.7%)had MPE,21 had(19.8%)TBPE,and 32(30.2%)were undiagnosed.Overall diagnostic yield of MT was 69.8%(MPE:75.9%,TBPE:48.8%,and IPE:75.0%,with diagnostic accuracies of 100%,87.5%,and 75.0%,respectively).Under thoracoscopy,single or multiple pleural nodules were observed in 81.1%and pleural adhesions in 34.0%with pleural effusions.The most common complication was chest pain(41.5%),followed by chest tightness(11.3%)and fever(10.4%).Multivariate logistic regression analyses showed effusion appearance[odds ratio(OR):0.001,95%CI:0.000-0.204;P=0.010]and carcinoembryonic antigen(OR:0.243,95%CI:0.081-0.728;P=0.011)as significant for differentiating MPE and TBPE,with area under the receiver operating characteristic curve of 0.977(95%CI:0.953-1.000;P<0.001).CONCLUSION MT is an effective,safe,and minimally invasive procedure with high diagnostic yield for pleural effusion of different causes.

Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation

Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half.