scholarly journals Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Azher Uddin ◽  
Bayazid Talukder ◽  
Mohammad Monirujjaman Khan ◽  
Atef Zaguia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Close Contact ◽  
World Health ◽  
Contact Tracing ◽  
X Rays ◽  
Health Crisis ◽  
Gastrointestinal Problems ◽  
The World ◽  
Health Organization

The world is facing a pandemic due to the coronavirus disease 2019 (COVID-19), named as per the World Health Organization. COVID-19 is caused by the virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was initially discovered in late December 2019 in Wuhan, China. Later, the virus had spread throughout the world within a few months. COVID-19 has become a global health crisis because millions of people worldwide are affected by this fatal virus. Fever, dry cough, and gastrointestinal problems are the most common signs of COVID-19. The disease is highly contagious, and affected people can easily spread the virus to those with whom they have close contact. Thus, contact tracing is a suitable solution to prevent the virus from spreading. The method of identifying all persons with whom a COVID-19-affected patient has come into contact in the last 2 weeks is called contact tracing. This study presents an investigation of a convolutional neural network (CNN), which makes the test faster and more reliable, to detect COVID-19 from chest X-ray (CXR) images. Because there are many studies in this field, the designed model focuses on increasing the accuracy level and uses a transfer learning approach and a custom model. Pretrained deep CNN models, such as VGG16, InceptionV3, MobileNetV2, and ResNet50, have been used for deep feature extraction. The performance measurement in this study was based on classification accuracy. The results of this study indicate that deep learning can recognize SARS-CoV-2 from CXR images. The designed model provided 93% accuracy and 98% validation accuracy, and the pretrained customized models such as MobileNetV2 obtained 97% accuracy, InceptionV3 obtained 98%, and VGG16 obtained 98% accuracy, respectively. Among these models, InceptionV3 has recorded the highest accuracy.

Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

2019 ◽  
Vol 10 (3) ◽  
pp. 60-73 ◽  
Author(s):  
Ravinder Ahuja ◽  
Daksh Jain ◽  
Deepanshu Sachdeva ◽  
Archit Garg ◽  
Chirag Rajput
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Automated System ◽  
World Health ◽  
American Sign ◽  
Hand Gestures ◽  
The World ◽  
Health Organization

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

scholarly journals Managing COVID-19 Pandemic in Teaching Hospitals: An Early Preparedness Setting

2021 ◽  
Vol 50 (4) ◽  
pp. 1165-1173
Author(s):  
Azmawati Mohammed Nawi ◽  
Azimatun Noor Aizuddin ◽  
Rozita Hod ◽  
Norfazilah Ahmad ◽  
Faiz Daud ◽  
...  
Keyword(s):  
Close Contact ◽  
Hospital Staff ◽  
World Health ◽  
Contact Tracing ◽  
Teaching Hospitals ◽  
Crisis Preparedness ◽  
The World ◽  
Government Hospitals ◽  
Health Organization ◽  
Early Establishment

The World Health Organization (WHO) declared the 2019-20 coronavirus disease (COVID-19) outbreak a pandemic on 11th of March 2020. The Ministry of Health, Malaysia has made preparations for the involvement of all government hospitals, including some teaching hospitals. This report elaborates and discusses the early establishment of the Hospital Canselor Tuanku Muhriz Crisis Preparedness and Response Centre (HCTM CPRC), highlighting how teaching hospitals function in handling the clinical and epidemiological management of COVID-19 among hospital staff. The setting comprises of four critical functions of the HCTM CPRC, namely case investigation, close contact tracing, surveillance for data reporting and risk communication. This report highlighted that a CPRC in teaching hospitals benefits not only the patients and the hospital administration but also all hospital staff, especially in managing COVID-19 pandemic emergency crisis.

scholarly journals Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

2021 ◽  
Vol 12 (3) ◽  
pp. 011-019
Author(s):  
Haris Uddin Sharif ◽  
Shaamim Udding Ahmed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Patient ◽  
World Health ◽  
X Rays ◽  
Popular Approach ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

scholarly journals Coupling multifunction drones with AI in the fight against the coronavirus pandemic

Computing ◽  
2021 ◽  
Author(s):  
Faris A. Almalki ◽  
Abdullah A. Alotaibi ◽  
Marios C. Angelides
Keyword(s):  
Neural Network ◽  
Close Contact ◽  
Elevation Angle ◽  
Face Mask ◽  
World Health ◽  
Ground Station ◽  
Health Authorities ◽  
The World ◽  
Health Organization ◽  
National Governments

AbstractWhen COVID-19 was declared as a pandemic by the World Health Organization on 11 March 2020, national governments and health authorities across the world begun considering different preventive measures to fight against the coronavirus outbreak. Researchers and tech companies worldwide have been striving to utilize advanced technologies to aid in the fight against the Covid-19 outbreak. This paper aims to couple multifunction drone with AI to deliver wireless services that will help the fight against the Coronavirus pandemic. The proposed drone-eye-system with its thermal imaging cameras and an AI framework utilizes a Convolutional Neural Network (CNN) with its Modified Artificial Neural Network (MANN) for face mask detection of people wearing masks in public. The system can perform basic diagnostic functions such as elevated body temperatures for helping minimize the risk of spreading the infection through close contact. The AI framework evolve an optimized elevation angle $$\uptheta $$ θ and altitude $${\mathrm{h}}_{\mathrm{t}}$$ h t to enhance wireless connectivity between a drone and a ground station, which in turn leads to better throughput and power consumption. The proposed framework has been developed using the MATLAB toolbox and shows promising results with an accuracy of face mask detection of 82.63%, with an F1-score of 0.98, and an enhanced by 10% link budget parameters.

scholarly journals CNN-LSTM Model for Verifying Predictions of Covid-19 Cases

2020 ◽  
pp. 25-32
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay ◽  
Tai-Hoon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
World Health ◽  
Human Beings ◽  
Short Period ◽  
Machine Learning Approach ◽  
The World ◽  
Working Day ◽  
Health Organization ◽  
Near Future

COVID-19 disease came to earth in December 2019 in Wuhan. It is increasing exponentially throughout the world and affected an enormous number of human beings. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. Clinical Doctors have been working on it 24 hours in the entire world. These doctors are testing whether the particular human has been affected with the disease using testing kit and other related process. Researchers have been working day-night for developing vaccine for the disease. Since the rate of affected people is so high, it is difficult for clinical doctors to check such a large number of coronavirus detected humans within reasonable time. This paper attempts to use Machine Learning Approach to build up model which will help clinical doctors for verification of disease within short period of time and also the paper attempts to predict growth of the disease in near future in the world. Two models were used for achieving this purpose- One is based on Convolutional Neural Network model where as another one consists of Convolutional Neural Network and Recurrent Neural Network. These two models are evaluated and compared for verifying the predicted result with respect to the original one. Experimental results indicate that the combined CNN-LSTM approach outperforms well over the other model.

TIRYAQ-E-ARBA (A POLY HERBAL UNANI FORMULATION) IN FIGHTING AGAINST COVID 19 LIKE CONDITIONS – UNANI PERSPECTIVE

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Nafisa Qibriya Khan ◽  
A. H. Farooqui ◽  
Syed Ayesha Fatima ◽  
Jalil Ahmad ◽  
Tausif S. Khan
Keyword(s):  
Respiratory Infections ◽  
Transmission Rate ◽  
Multiple Organ ◽  
World Health ◽  
Upper Respiratory Tract ◽  
Health Crisis ◽  
Unani Medicine ◽  
The World ◽  
Health Organization ◽  
Almost All

Coronavirus disease 2019 (COVID-19) is a pandemic disease of modern time with unique and rapid transmission rate and affected almost all the nations without respecting any border. Coronavirus disease 2019 (COVID-19) is arguably the biggest health crisis the world has faced in 21st century. It is an infectious disease and declared pandemic by the World Health Organization. The coronavirus disease 2019 (COVID-19) outbreak, which originated in Wuhan, China, has now spread to 192 countries and administrative regions infecting nearly 800,000 individuals of all ages as of 31 March 2020. Though most infected individuals exhibit mild symptoms including fever, upper respiratory tract symptoms, shortness of breath, and diarrhoea, or are asymptomatic altogether, severe cases of infection can lead to pneumonia, multiple organ failure, and death. Globally, at least 7900 deaths have been directly attributed to COVID19, and this number is expected to rise with the ongoing epidemic. This is particularly crucial as the current outbreak involves a new pathogen (SARS-CoV-2), on which limited knowledge exists of its infectivity and clinical profile. Research is in progress on therapeutic efficacy of various agents including anti-malarials (Chloroquine and Hydroxychloroquine), antiviral drugs, and convalescent serum of recovered patients. Unani system of medicine is one of the traditional systems of medicine which is being explored for providing preventive, supportive and rehabilitative care to patients. Unani system of medicine has a detailed description of drugs that are utilized in many infectious diseases, including respiratory infections. Immune response is essential to eliminate virus and to preclude disease progression to severe stages. Therefore, it is important to summarize the evidence regarding the preventive measures, control options such as immune-stimulator and prophylactic treatment in Unani medicine against Covid19. This review summarizes various pharmacological actions of Unani formulation Tiryaq-e-Arba in Unani literature and various reported pharmacological activities which can possibly provide prevention, control and reduction of complications of this deadly disease.

scholarly journals Four pandemics: lessons learned, lessons lost

2020 ◽  
Keyword(s):  
Public Health ◽  
Lessons Learned ◽  
World Health ◽  
Swine Flu ◽  
Global Public Health ◽  
Health Crisis ◽  
The World ◽  
Spanish Flu ◽  
Future Events ◽  
Health Organization

In the past 100 years, the world has faced four distinctly different pandemics: the Spanish flu of 1918-1919, the SARS pandemic of 2003, the H1N1 or “swine flu” pandemic of 2012, and the ongoing COVID-19 pandemic. Each public health crisis exposed specific systemic shortfalls and provided public health lessons for future events. The Spanish flu revealed a nursing shortage and led to a great appreciation of nursing as a profession. SARS showed the importance of having frontline clinicians be able to work with regulators and those producing guidelines. H1N1 raised questions about the nature of a global organization such as the World Health Organization in terms of the benefits and potential disadvantages of leading the fight against a long-term global public health threat. In the era of COVID-19, it seems apparent that we are learning about both the blessing and curse of social media.

scholarly journals The Covid infodemic

2021 ◽  
Author(s):  
Ken Hyland ◽  
Feng (Kevin) Jiang
Keyword(s):  
Social Anxiety ◽  
Global Health ◽  
Medical Research ◽  
World Health Organization ◽  
Research Methods ◽  
World Health ◽  
Health Crisis ◽  
The World ◽  
Health Organization ◽  
Highly Cited

Abstract Covid-19, the greatest global health crisis for a century, brought a new immediacy and urgency to international bio-medical research. The pandemic generated intense competition to produce a vaccine and contain the virus, creating what the World Health Organization referred to as an ‘infodemic’ of published output. In this frantic atmosphere, researchers were keen to get their research noticed. In this paper, we explore whether this enthusiasm influenced the rhetorical presentation of research and encouraged scientists to “sell” their studies. Examining a corpus of the most highly cited SCI articles on the virus published in the first seven months of 2020, we explore authors’ use of hyperbolic and promotional language to boost aspects of their research. Our results show a significant increase in hype to stress certainty, contribution, novelty and potential, especially regarding research methods, outcomes and primacy. Our study sheds light on scientific persuasion at a time of intense social anxiety.

scholarly journals Penggunaan Aplikasi Google Classroom sebagai Metode Pembelajaran Daring Selama Pandemi Covid-19

2021 ◽  
Vol 14 (2) ◽  
pp. 11-17
Author(s):  
Mirza Ghulamudin Ghulamudin ◽  
Maufur ◽  
Beni Habibi
Keyword(s):  
Distance Learning ◽  
World Health ◽  
Ministry Of Education ◽  
Health Crisis ◽  
Use Of Technology ◽  
The World ◽  
Teachers And Students ◽  
The Government ◽  
Health Organization ◽  
In The Beginning

Covid-19 has now attacked Indonesia, where the spread of the disease is very fast. Not only in Indonesia, but all corners of the world are currently experiencing a health crisis. In the beginning, the spread of Covid-19 had an impact on economic activity which began to sluggish. This also has an impact on the education system in Indonesia. Until several countries decided to close schools and universities. In an effort to prevent the spread of covid-19, the World Health Organization (WHO) recommends temporarily stopping activities that would potentially cause crowds. Even during the outbreak, covid-19 in Indonesia, there were many ways that the government did to prevent its spread through social distancing. Kemendikbud instructed through the Ministry of Education and Culture (Kemendikbud) Directorate of Higher Education Circular No. 1 of 2020 concerning the prevention of the spread of covid-19 in the world of Education to organize distance learning and advise students to learn from their homes. Teachers and students are starting to be required to follow the current situation by using technology as a distance learning medium. One of the media that is being favored by teachers as a learning medium is the Google Classroom application. This application is an application that can make it easier for students and teachers to create effective learning. Given that students today are a generation who are very familiar with the use of technology. The use of technology in learning is an alternative method used by teachers during the Covid-19 Pandemic.

scholarly journals Global Governance and the WHO’s Mandate Post-COVID-19 Crisis

2021 ◽  
Author(s):  
Jean Vilbert
Keyword(s):  
Global Health ◽  
Negative Relationship ◽  
World Health ◽  
First Year ◽  
Health Crisis ◽  
Nation States ◽  
Democratic Principle ◽  
The World ◽  
Distribution Of Power ◽  
Health Organization

The COVID-19 has renovated the debate about global health governance. A number of scholars have proposed that the World Health Organization should assume the position of a central coordinator with hierarchical powers, demanding nation-states to “share their sovereignty”. This article presents four main objections to this project. First, when international institutions receive leverage, they use to impose “one-size-fits-all” policies, which conflicts with the characteristic heterogeny across countries. Second, geopolitical questions and the distribution of power in multilateral institutions put developing countries in a position of vulnerability within a hierarchical order. Third, the risk of crowding out parallel initiatives, especially from non-state actors. Fourth, decisions about health can have a major impact on countries, which may thwart the internal democratic principle. A Pareto improvement would be possible by strengthening the WHO’s operational capacity and its ability to issue technical guidance and coordinate with countries. To test this hypothesis, this study analyses the possible influence of the WHO’s guidance in the first year of the coronavirus health crisis, from January 2020 to January 2021, in 37 countries reported in the World Values Survey Wave 7 (2017-2020). The OLS regression performed shows a statistically significant negative relationship between the trust in the WHO, assumed as a proxy for the level of the organization's penetration, and the number of cases of COVID-19 (per million people) in the countries of the sample. These findings reinforce the hypothesis that there is a valid case for the countries to strengthen the WHO’s mandate post-COVID-19, but they should enhance the operations of provision of reliable information and support. Nation-states, in particular the developing ones, should eschew the temptation to create a hierarchical global health structure, which may not only fail due to countries’ asymmetries but is likely to create losers in the process.

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