Design of Covid19 Detection Based on Relative Eccentric Feature Selection Using Deep Vectored Regressive Neural Network for Corona Virus

The world has seen various diseases in different variants, numerous pandemics in the twentieth century like covid-19. Fly infections are the fundamental driver of contaminations. An epidemic known as COVID-19 has been declared, and it has had a significant impact on society and the global economy. The diagnosis of Covid19 or non-Covid-19 cases early detection at the correct separation at the lowest cost early stages of the disease is one of the major problems in the current coronavirus pandemic. To address this problem, the proposed Deep learning and Design of covid19 detection based on Relative Eccentric Feature Selection (REFS) Using Deep Vectorized Regressive Neural Network (DVRNN) for corona virus the early detection of the Covid19 virus. Initially collects the covid19 sample test dataset, then the raw dataset trained into preliminary process is used to remove unwanted noise. After that preliminary processed dataset trained into the feature selection process is done to identify the best features of covid19 using Ensemble recursive feature selection. Further, the proposed DVRNN algorithm is done to classify the accurate detection of coronavirus. The proposed model would be useful for the timely and accurate identification of the coronavirus at different stages. Therefore it can detect the accurate results of covid19 effectively and accomplish good performance compared with previous methods. © 2022 IEEE.

Accurately Detecting Trace-Level Infectious Agents by an Electro-Enhanced Graphene Transistor

For epidemic prevention and control, molecular diagnostic techniques such as field-effect transistor (FET) biosensors is developed for rapid screening of infectious agents, including Mycobacterium tuberculosis, SARS-CoV-2, rhinovirus, and others. They obtain results within a few minutes but exhibit diminished sensitivity (<75%) in unprocessed biological samples due to insufficient recognition of low-abundance analytes. Here, an electro-enhanced strategy is developed for the precise detection of trace-level infectious agents by liquid-gate graphene field-effect transistors (LG-GFETs). The applied gate bias preconcentrates analytes electrostatically at the sensing interface, contributing to a 10-fold signal enhancement and a limit of detection down to 5 × 10−16 g mL−1 MPT64 protein in serum. Of 402 participants, sensitivity in tuberculosis, COVID-19 and human rhinovirus assays reached 97.3% (181 of 186), and specificity is 98.6% (213 of 216) with a response time of <60 s. This study solves a long-standing dilemma that response speed and result accuracy of molecular diagnostics undergo trade-offs in unprocessed biological samples, holding unique promise in high-quality and population-wide screening of infectious diseases. © 2023 Wiley-VCH GmbH.

SARS-CoV-2 in Terms of Medical Microbiology Laboratory.

Coronaviruses (CoV), which are in the Coronaviridae family, cause different severity of gastrointestinal, respiratory and systemic diseases in wild and domestic animals, and can lead to different clinical manifestations, ranging from colds to pneumonia, depending on immunity. To date, seven types of coronavirus have been identified as infectious agents in humans;of these, HCoV 229E, HCoV NL63, HCoV HKU1 and HCoV OC43 typically cause cold symptoms in immunocompetent individuals, while SARS-CoV (Severe Acute Respiratory Syndrome Coronavirus) and MERS-CoV (Middle East Respiratory Syndrome Coronavirus) is zoonotic and cause severe respiratory diseases and deaths. SARS-CoV-2, the causative agent of COVID-19, is the seventh coronavirus identified as an infection agent in humans, which started in December 2019 in Wuhan, Hubei Province of China and was identified as a pandemic in a short time. Since the World Health Organization (WHO) defines SARS-CoV-2-sourced COVID-19 as a pandemic, and because of the increasing number of cases and deaths worldwide, structure of the novel virus and viral diagnosis methods gained importance respectively for vaccine studies and for controlling the outbreak caused by the virus.Copyright © 2020 Ankara Pediatric Hematology Oncology Training and Research Hospital. All rights reserved.

Statistical Processing of Relationship Between Biomarkers and Disease Severity Caused by COVID-19 Infection-Delta Variant

Severe infectious disease caused by acute respiratory syndrome, COVID-19 (SARS-CoV-2), spread rapidly worldwide, infecting several million people. According to scientific data, the disease develops through several different stages. After 2–4 days of infection and disease development, the lower respiratory tract is attacked and in a relatively short time interstitial pneumonia develops in a certain number of patients (with genetic predisposition between 5 and 10% of cases). Patients infected with COVID-19 have symptoms such as very high temperatures, fever, persistent cough, joint and bone pain, in some cases diarrhea, and loss of appetite and taste. Disease monitoring should primarily include erythrocyte sedimentation rate, leukocyte count, leukocyte count formula, C-reactive protein (CRP), determination of troponin I (hsTnI) and T (cTnT) levels, N-terminal pro-B natriuretic peptide (NT-proBNP), fibrinogen, and D-dimer level. Previous studies have shown that in pneumonia developed from chronic and acute obstructive pulmonary infections, high levels of D-dimer are observed in patients, and it is suggested that this parameter can be used as a specific prognostic biomarker, and the values higher than > 1000 ng/ml represent increased risk factors for mortality in patients with COVID-19. Because vascular thrombosis affects the promotion of an unfavorable clinical progression for the patient, the identification of early and accurate predictors of the worst outcome seems to be essential for timely and appropriate anticoagulant treatment in patients with SARS-CoV-2 infection. Overall, these data suggest that acute myocardial damage, or heart failure, may be an important indicator of disease severity and adverse prognosis in patients with COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Using Deep Learning Algorithms to Diagnose Coronavirus Disease (COVID-19)

With the rapid development in the area of Machine Learning (ML) and Deep learning, it is important to exploit these tools to contribute to mitigating the effects of the coronavirus pandemic. Early diagnosis of the presence of this virus in the human body can be crucially helpful to healthcare professionals. In this paper, three well-known Convolutional Neural Network deep learning algorithms (VGGNet 16, GoogleNet and ResNet50) are applied to measure their ability to distinguish COVID-19 patients from other patients and to evaluate the best performance among these algorithms with a large dataset. Two stages are conducted, the first stage with 14994 x-ray images and the second one with 33178. Each model has been applied with different batch sizes 16, 32 and 64 in each stage to measure the impact of data size and batch size factors on the accuracy results. The second stage achieved accuracy better than the first one and the 64 batch size gain best results than the 16 and 32. ResNet50 achieves a high rate of 99.31, GoogleNet model achieves 95.55, while VGG16 achieves 96.5. Ultimately, the results affect the process of expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, and resulting in improved clinical outcomes © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Identification of Coronavirus & Pneumonia from Chest x-ray Scans using Deep Multilayered CNN

Recently, COVID-19 disease carried out by the SARS-CoV-2 virus appeared as a pandemic across the world. The traditional diagnostic techniques are facing a hard time detecting the virus efficiently at an early stage. In this context, chest x-ray scans can be useful for diagnostic prediction. Therefore, in this paper, a deep multi-layered convolution neural network has been proposed to analyze the chest x-ray scans effectively for detecting COVID-19 and pneumonia accurately. The proposed approach has been applied on multiple benchmark datasets and the experimental results define the effectiveness of the proposed approach. © 2021 IEEE.

Medical diagnosis of COVID-19 using blood tests and machine learning

Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2), colloquially known as Coronavirus surfaced in late 2019 and is an extremely dangerous disease. RT-PCR (Reverse transcription Polymerase Chain Reaction) tests are extensively used in COVID-19 diagnosis. However, they are prone to a lot of false negatives and erroneous results. Hence, alternate methods are being researched and discovered for the detection of this infectious disease. We diagnose and forecast COVID-19 with the help of routine blood tests and Artificial Intelligence in this paper. The COVID-19 patient dataset was obtained from Israelita Albert Einstein Hospital, Brazil. Logistic regression, random forest, k nearest neighbours and Xgboost were the classifiers used for prediction. Since the dataset was extremely unbalanced, a technique called SMOTE was used to perform oversampling. Random forest obtained optimal results with an accuracy of 92%. The most important parameters according to the study were leukocytes, eosinophils, platelets and monocytes. This preliminary COVID-19 detection can be utilised in conjunction with RT-PCR testing to improve sensitivity, as well as in further pandemic outbreaks. © 2022 Institute of Physics Publishing. All rights reserved.

Diagnosis of COVID-19 from chest X-ray images using Deep Learning Model

COVID-19 (Coronavirus-2019) is a disastrous pandemic which has affected the whole world damaging the whole ecosystem specially health. Researchers around the world have been contributing to the advancement in these conditions around the world. Medical practitioners have found that chest X-Ray images can used to detect whether a person suffers from covid-19 or not due to anomalies in chest radiography images. Continuing this motivation we have taken around 700 chest X ray images from different resources and applied two different transfer learning techniques to build a model which can detect the existence of covid-19 in a person. It uses VGG16 and Resnet50 deep learning models which utilize transfer learning to train their parameters. We have trained our both models for 50 epochs. VGG16 gives 76% of accuracy on test data and Resnet50 gives 85% accuracy on test data. We have tried to engineer thresholds for probability of classification thus changing specificity and sensitivity and also evaluated our models on various metrics such as classification report, confusion matrix heatmap, roc-auc score. This is by no means to be used for any medical procedures but can help other researchers to take some useful insights from it and carry forward the learning in building something which is production ready for contribution in our fight against COVID-19. © 2021 IEEE.