Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

The extraction of features fromunstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease.Furthermore,an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems.In this paper,an improved Term Weighting technique combined with Parts-Of-Speech(POS)Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease.Term Frequency-Inverse Document Frequency(TF-IDF)is the most often used term weighting scheme(TWS).However,TF-IDF has several developments to improve its drawbacks,in particular,it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data.In this research,we proposed a modification term weighting scheme:RTF-C-IEF and compare the proposed model with four extraction methods:TF,TF-IDF,TF-IHF,and TF-IEF.The experiment was conducted on two new datasets for COVID-19 patients.The first datasetwas collected from government hospitals in Iraq with 3053 clinical records,and the second dataset with 1446 clinical reports,was collected from several different websites.Based on the experimental results using several popular classifiers applied to the datasets of Covid-19,we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments.Further,the modifiedRTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments.Thus,the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Differential diagnosis of COVID-19 at the chest computed tomography scan:A review with special focus on cancer patients

BACKGROUND Given the several radiological features shared by coronavirus disease 2019 pneumonia and other infective or non-infective diseases with lung involvement,the differential diagnosis is often tricky,and no unequivocal tool exists to help the radiologist in the proper diagnosis.Computed tomography is considered the gold standard in detecting pulmonary illness caused by severe acute respiratory syndrome coronavirus 2.AIM To conduct a systematic review including the available studies evaluating computed tomography similarities and discrepancies between coronavirus disease 2019 pneumonia and other pulmonary illness,then providing a discussion focus on cancer patients.METHODS Using pertinent keywords,we performed a systematic review using PubMed to select relevant studies published until October 30,2020.RESULTS Of the identified 133 studies,18 were eligible and included in this review.CONCLUSION Ground-glass opacity and consolidations are the most common computed tomography lesions in coronavirus disease 2019 pneumonia and other respiratory diseases.Only two studies included cancer patients,and the differential diagnosis with early lung cancer and radiation pneumonitis was performed.A single lesion associated with pleural effusion and lymphadenopathies in lung cancer and the onset of the lesions in the radiation field in the case of radiation pneumonitis allowed the differential diagnosis.Nevertheless,the studies were heterogeneous,and the type and prevalence of lesions,distributions,morphology,evolution,and additional signs,together with epidemiological,clinical,and laboratory findings,are crucial to help in the differential diagnosis.

Retrospective Diagnosis of COVID-19 in an Asymptomatic Patient Undergoing Emergency Surgery

Background: With reports of higher mortality and complications occurring in patients with perioperative 2019 novel coronarvirus disease (COVID-19), most elective surgeries have been postponed. However, evidence regarding emergency surgeries in patients with COVID-19 remains scarce. We report the case of a patient with asymptomatic perioperative COVID-19, presenting with an acute abdomen requiring surgery. Case: A 25-year-old male, with a prior nasopharyngeal swab that was negative for SARS-CoV-2, presented with classical signs and symptoms of acute appendicitis. Clinical examination and investigations were not suggestive of COVID-19 infection. He underwent laparoscopic appendicectomy with infection control precautions. Post-operatively, he was found to be positive for SARS-CoV-2 but remained asymptomatic and had an uneventful recovery. Conclusion: In asymptomatic individuals with higher risks, negative test results should be viewed cautiously. The benefits of urgent surgical interventions must be weighed against the risks of complications due to perioperative COVID-19 in these patients.

A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

Many respiratory infections around the world have been caused by coronaviruses.COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate.There is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems.Machine learning(ML)has been used to examine chest X-ray frames.In this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal person.First,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature extraction.Second,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN models.These selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray images.The proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

Bronchoscopy for diagnosis of COVID-19 with respiratory failure:A case report

BACKGROUND Although the imaging features of coronavirus disease 2019(COVID-19)are starting to be well determined,what actually occurs within the bronchi is poorly known.Here,we report the processes and findings of bronchoscopy in a patient with COVID-19 accompanied by respiratory failure.CASE SUMMARY A 65-year-old male patient was admitted to the Hainan General Hospital on February 3,2020 for fever and shortness of breath for 13 d that worsened for the last 2 d.The severe acute respiratory syndrome coronavirus 2 nucleic acid test was positive.Routine blood examination on February 28 showed a white blood cell count of 11.02×109/L,86.9%of neutrophils,6.4%of lymphocytes,absolute lymphocyte count of 0.71×109/L,procalcitonin of 2.260 ng/mL,and C-reactive protein of 142.61 mg/L.Oxygen saturation was 46%at baseline and turned to 94%after ventilation.The patient underwent video bronchoscopy.The tracheal cartilage ring was clear,and no deformity was found in the lumen.The trachea and bilateral bronchi were patent,while the mucosa was with slight hyperemia;no neoplasm or ulcer was found.Moderate amounts of white gelatinous secretions were found in the dorsal segment of the left inferior lobe,and the bronchial lumen was patent after sputum aspiration.The right inferior lobe was found with hyperemia and mucosal erosion,with white gelatinous secretion attachment.The patient’s condition did not improve after the application of therapeutic bronchoscopy.CONCLUSION For patients with COVID-19 and respiratory failure,bronchoscopy can be performed under mechanical ventilation to clarify the airway conditions.Protection should be worn during the process.Considering the risk of infection,it is not necessary to perform bronchoscopy in the mild to moderate COVID-19 patients.

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death toll.Soon nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

Role of artificial intelligence in the diagnosis of COVID-19:A mini review

The ongoing COVID-19 pandemic due to severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection has resulted in a significant public health care system crisis.This disease has resulted in devastating damage to human lives and significant disruption in economies.Use of“machine-learning”algorithms as tools of artificial intelligence may help identify a suspected or infected individual with an estimation of chances of survival.These algorithms make use of recorded observational data including medical histories,patient demographics as well as any related data on COVID-19.

Practical Guideline for Screening the Patients with SARS-CoV-2 Infection and Persian Gulf Criteria for Diagnosis of COVID-19

The new Coronavirus disease or COVID-19 is a contagious viral/immune-logical systemic disorder with predominantly respiratory features caused by human infection with SARS-CoV-2, which is rapidly spreading from person-to-person all around the world as a pandemic. The new outbreak of COVID-19 first appeared in Wuhan, China in December 2019. This virus is transmitted from human to human in various ways including air, aerosol, touching, and fecal-oral ways. The SARS-CoV-2 survives for several days in the environment. The SARS-CoV-2 virus multiplies within the cells of mouth-throat or nose-throat, and despite the production of antibodies by the human immune system, if the virus continues to multiply and progress, it will enter the bloodstream and reach its target organ, the lungs. It takes an incubation period of one to fourteen days for the initial symptoms/signs of disease to appear as fever, dry cough, and fatigue. Finally, shortness of breath due to pneumonia/pneumonitis with or without Acute Respiratory Distress Syndrome (ARDS) causes the patient to be hospitalized and transferred to ICU. Older people with underlying disorders account for the majority of deaths from COVID-19, while children under the age of 15 - 20 are the main carriers of the SARS-CoV-2. About 40% of patients with COVID-19 are asymptomatic and, 40% mild, 15%;severe, and 5% are critical COVID-19. COVID-19 Molecular Diagnostic Tests and COVID-19 Antibody Tests are two types of diagnostic kit tests for identification of the SARS-CoV-2 and the High Resolution Computerised Tomography (HRCT) scanning of lungs is the best imaging method for detecting pneumonia/pneumonitis and assessing its severity. This paper is intended to present a health system called COVID-19 Referral System for screening and developing very sensitive diagnostic criteria as Persian Gulf Criteria for diagnosis of COVID-19. By using these two methods and performing the SARS-CoV-2 kit tests more and more widely, and performing accurate isolation of patients and virus carriers and complete quarantine of red zones, it is possible to successfully control the SARS-CoV-2 epidemics.

Molecular and serology methods in the diagnosis of COVID-19: An overview

Coronavirus disease-19(COVID-19)has become a pandemic,being a global health concern since December 2019 when the first cases were reported.Severe acute respiratory syndrome coronavirus 2,the COVID-19 causal agent,is aβ-coronavirus that has on its surface the spike protein,which helps in its virulence and pathogenicity towards the host.Thus,effective and applicable diagnostic methods to this disease come as an important tool for the management of the patients.The use of the molecular technique PCR,which allows the detection of the viral RNA through nasopharyngeal swabs,is considered the gold standard test for the diagnosis of COVID-19.Moreover,serological methods,such as enzyme-linked immunosorbent assays and rapid tests,are able to detect severe acute respiratory syndrome coronavirus 2-specific immunoglobulin A,immunoglobulin M,and immunoglobulin G in positive patients,being important alternative techniques for the diagnostic establishment and epidemiological surveillance.On the other hand,reverse transcription loop-mediated isothermal amplification also proved to be a useful diagnostic method for the infection,mainly because it does not require a sophisticated laboratory apparatus and has similar specificity and sensitivity to PCR.Complementarily,imaging exams provide findings of typical pneumonia,such as the ground-glass opacity radiological pattern on chest computed tomography scanning,which along with laboratory tests assist in the diagnosis of COVID-19.

SARS-COV-2 Rapid Antigen Test in Comparison with RT-PCR for Laboratory Diagnosis of COVID-19 in a Southwest State of Nigeria

Objectives: Rapid and accurate identification of persons infected with SARS-CoV-2 which causes COVID-19 is key to managing the pandemic. The urgent need to scale up access to COVID-19 testing in Nigeria has led to the government’s introduction of the use of COVID-19 Ag rapid diagnostic test (RDT) across various settings in the country. However, field performance evaluation of the rapid SARS-CoV-2 antigen detection test is required to be conducted periodically and compared with the gold standard real-time reverse transcription-polymerase chain reaction (RT-PCR) test for diagnosis of COVID-19 cases. Design: A prospective COVID-19 screening and un-blinded verification of the performance of the STANDARD Q COVID-19 Ag test kit. Setting: The rapid SARS-CoV-2 antigen detection test, StandardTM Q COVID-19 Ag kit was compared with the RT-PCR test for detection of SARS-CoV-2 in nasopharyngeal samples for COVID-19 screening from persons and personnel attending a national youth camp orientation exercise during the second wave of the COVID-19 outbreak (January to March 2021) in Ondo state, southwest Nigeria. Participants: Three hundred fifty-one persons and personnel were screened for COVID-19 infection. Results: Of 351 respondents screened, 68 (19.4%) were positive, and 264 (75.2%) were negative for both COVID-19 Ag RDT and RT-PCR assay. The rapid SARS-CoV-2 antigen detection test’s sensitivity and specificity were 78.16% (95% CI = 68.02% - 86.31%) and 100.0% (95% CI = 98.61% - 100.0%), respectively and the diagnostic accuracy was 94.59% (95% CI: 92 - 97). Respondents that were symptomatic had a higher test sensitivity of 78.6% (49.2 - 95.3) compared to those without symptoms 78.1% (66.9 - 86.9) (p Conclusions: Our study shows evidence that StandardTM Q COVID-19 Ag kit can be an appropriate rapid antigen test that could be used to screen for positive COVID-19 tests to guide decision-making for clinical management of persons infected with COVID-19, especially for closed settings and other clinical care settings.

Principles of Diagnosis and Treatment of Patients with Diagnosis of Covid-19 and Developing Viral Pneumonia

The article provides information on our achievements in the application of modern diagnostic methods and modern methods of treating patients with viral pneumonia,confirmed by covid-19.For this,statistical data of 2,000 patients were used.Of the 2,000 patients treated,920 were men,1,070 were women and 10 were children.Viral pneumonia-glaucoma syndrome in 1650 out of 2,000 patients with 10-20%damage;In 350,the diagnosis of viral pneumonia-frostbite syndrome with 50-85%damage,CRDS,respiratory failure was confirmed.Thus,50 out of 350 patients treated at the intensive care unit(ICU)out of 2,000 were intubated and connected to artificial ventilation.The research was carried out in 3 stages:I stage-admission to the intensive care unit;II stage-from the day of intubation to spontaneous breathing(7-14 days);and III stage-covers the period of extubation and recovery.The results of clinical,functional,hemodynamic and echocardiographic studies of the patients participating in the examination were analyzed.Also,the patients underwent bacteriological research studied the sensitivity to antibiotics.In addition,the composition of blood gases and the oxygenation index-Carrico were studied.

Recent progress on rapid diagnosis of COVID-19 by point-of-care testing platforms

The outbreak of COVID-19 has drawn great attention around the world.SARS-CoV-2 is a highly infectious virus with occult transmission by many mutations and a long incubation period.In particular,the emergence of asymptomatic infections has made the epidemic even more severe.Therefore,early diagnosis and timely management of suspected cases are essential measures to control the spread of the virus.Developing simple,portable,and accurate diagnostic techniques for SARS-CoV-2 is the key to epidemic prevention.The advantages of point-of-care testing technology make it play an increasingly important role in viral detection and screening.This review summarizes the point-of-care testing platforms developed by nucleic acid detection,immunological detection,and nanomaterial-based biosensors detection.Furthermore,this paper provides a prospect for designing future highly accurate,cheap,and convenient SARS-CoV-2 diagnostic technology.

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

A Case of Investigation and Diagnosis of Immune Thrombocytopenic Purpura After Vaccination of COVID-19 Inactivated Vaccine

Objective:Analyze the relationship between inoculating one case of the COVID-19 inactivated vaccine(Vero cell)and immune thrombocytopenic purpura to provide a reference for the standardized handling of adverse events following immunization.Methods:According to the"National Monitoring Program for Suspected Adverse Reactions to Vaccinations,"an on-site investigation,data collection and analysis,expert group diagnosis,and medical association assessment were conducted on a case of immune thrombocytopenic purpura in District A of Chongqing after vaccination with the inactivated COVID-19 vaccine.The assessment report was delivered to the three relevant parties,the case was reviewed,and the experience was summarized.Results:The investigation and diagnosis by the district-level vaccination abnormal reaction expert group concluded that the disease that occurred after vaccination with the COVID-19 inactivated vaccine was secondary immune thrombocytopenic purpura,an abnormal reaction to the vaccination.The medical damage was classified as Level II Grade B.The vaccine production enterprise raised objections to this conclusion.After re-assessment by the municipal-level medical association,the conclusion was consistent with that of the district-level medical association.The vaccine production enterprise did not raise any further objections.Conclusion:Through active collaboration among district and municipal-level medical associations,disease control institutions,and vaccination units,the recipients have been promptly and effectively treated,providing financial support for their subsequent treatment and safeguarding their rights.The investigation and disposal procedures for adverse events following immunization in Chongqing are clear,and the mechanism is sound.It is necessary to continue strengthening the monitoring of adverse events following immunization according to the existing plan and to ensure timely and standardized handling.Simultaneously,it is crucial to strengthen vaccine management and vaccination management.

Diagnosis,treatment protocols,and outcomes of liver transplant recipients infected with COVID-19

Several cases of fatal pneumonia during November 2019 were linked initially to severe acute respiratory syndrome coronavirus 2,which the World Health Organization later designated as coronavirus disease 2019(COVID-19).The World Health Organization declared COVID-19 as a pandemic on March 11,2020.In the general population,COVID-19 severity can range from asymptomatic/mild symptoms to seriously ill.Its mortality rate could be as high as 49%.The Centers for Disease Control and Prevention have acknowledged that people with specific underlying medical conditions,among those who need immunosuppression after solid organ transplantation(SOT),are at an increased risk of developing severe illness from COVID-19.Liver transplantation is the second most prevalent SOT globally.Due to their immunosuppressed state,liver transplant(LT)recipients are more susceptible to serious infections.Therefore,comorbidities and prolonged immunosuppression among SOT recipients enhance the likelihood of severe COVID-19.It is crucial to comprehend the clinical picture,immunosuppressive management,prognosis,and prophylaxis of COVID-19 infection because it may pose a danger to transplant recipients.This review described the clinical and laboratory findings of COVID-19 in LT recipients and the risk factors for severe disease in this population group.In the following sections,we discussed current COVID-19 therapy choices,reviewed standard practice in modifying immunosuppressant regimens,and outlined the safety and efficacy of currently licensed drugs for inpatient and outpatient management.Additionally,we explored the clinical outcomes of COVID-19 in LT recipients and mentioned the efficacy and safety of vaccination use.

Comparative Evaluation of RT-PCR Kits Available in Sri Lanka for Diagnosis of COVID-19

Identification of gene targets by real-time reverse transcriptase PCR(rRT-PCR)is considered as the gold standard for diagnosis of se-vere acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections.Although many commercial rRT-PCR kits are currently used in Sri Lanka,analytical performance of these kits have not been investigated adequately.Therefore,the objective of the present study was to evaluate the analytical performance of rRT-PCR kits used in the laboratory of the Faculty of Medicine,University of Jaffna(five kits).Performance of the five rRT-PCR kits selected for this study was compared with the CDC 2019-Novel Coronavirus(2019-nCoV)RT-PCR Diagnostic Panel as reference standard.The sensitivity,specificity,positive predictive value,negative predictive value and Cohen’sκcoefficient of the five different commercial kits were analyzed.SARS-CoV-2 positive(62)and negative(32)respiratory sam-ples collected respectively from symptomatic individuals and asymptomatic healthy individuals were used in this study.Comparison of the cycle threshold(Ct)values of the five commercial kits revealed heterogeneity.Among them,the TaqPathTM kit showed the highest sensitivity(98.4%)and interrater reliability(0.976).The HBRT-COVID-19 kit showed the lowest sensitivity(91.9%),specificity(93.7%)and interrater reliability(0.838).Although the five RT-PCR kits exhibited varying sensitivity,specificity and Ct values,all of them are suitable for the routine diagnosis of SARS-CoV-2 infections as all values were higher than 90%.

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.

Impact of COVID-19 on Biological Diagnosis of Malaria: Case of the Thierno Mouhamadoul Mansour Barro Hospital in Mbour, Senegal

Background: In Africa, malaria-endemic regions have not been spared from COVID-19 outbreak which emerged in the first quarter of 2020. This pandemic has shown clinical and therapeutic similarities with malaria. This following study sought to determine the impact of COVID-19 on the malaria diagnosis. Method: A review of laboratory registers and an exploitation of the District Health Information Software 2 (DHIS2) to collect information on the diagnosis of malaria by microscopy and by rapid diagnostic test (RDT), but also that of COVID-19 was done from 2017 to 2021 at the Thierno Mouhamadoul Mansour Hospital in Mbour, Senegal. Results: In 2017, 199 Thick drops (TDs) and 1852 RDTs were performed for malaria diagnosis. In 2018, it was 2352 malaria tests with 2138 RDTs and 214 TDs, before reaching a peak of 3943 tests in 2019 including 3742 RDTs and 201 TDs. By 2020, 2263 tests were performed with 2097 malaria RDTs, 158 TDs and 8 COVID RDTs. The latter increased significantly in 2021, reaching 444 COVID RDTs, while TDs and malaria RDT kept decreasing to 147 and 1036 respectively. Positive TDs were higher in 2020 (11.4%) compared to 2017 (3.5%), 2018 (1.4%), 2019 (6.5%) and 2021 (6.8%). For malaria RDTs, a decrease in the number of positive tests was noted between 2017 (4.5%) and 2021 (1.3%). The COVID RDTs were all negative in 2020, 29.5% were positive and 4.1% were undetermined in 2021. Conclusion: COVID-19 has led to changes in efforts to diagnose malaria as well as an increase in malaria prevalence directed towards children under 5 years of age.