scholarly journals CHEST X-RAY FINDINGS IN COVID-19 PNEUMONIA: A PICTORIAL REVIEW

2020 ◽  
pp. 9-11
Author(s):  
Zohra Ahmad ◽  
Parul Dutta ◽  
Deepjyoti Das Choudhury ◽  
Satabdi Kalita ◽  
Zohaib Hussain ◽  
...  
Keyword(s):  
Virus Disease ◽  
Vital Role ◽  
World Health ◽  
Thoracic Imaging ◽  
X Ray ◽  
Pictorial Review ◽  
Care Workers ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

Corona Virus Disease 19 or COVID-19, was first detected in Wuhan province in China in December 2019 and reported to the World Health Organization (WHO) on December 31, 2019 [1]. It was declared a pandemic on March 11th, 2020 [2] and has till now affected 40 million people all around the world resulting in 1.1 million deaths (as of 18th Oct, 2020) [3]. As the world is reeling under the burden of the disease, it has been imperative for the radiologists to be familiar with the imaging appearance of the disease. Thoracic imaging with chest X-ray and CT is the key modality for the diagnosis and management of respiratory diseases. Although CT is more sensitive, the immense challenge of disinfection control in the modality may disrupt the service availability and portable X-ray may be considered to minimize the risk [4]. Use of portable X-ray has played a vital role in all the areas around the world during this pandemic. The purpose of this pictorial review is to represent the frequently encountered features and abnormalities in chest X-ray and strengthen the knowledge of the health-care workers in this war against the pandemic.

Identifikasi Penderita COVID-19 Berdasarkan Chest X-Ray Menggunakan Algoritma Jaringan Syaraf Tiruan Backpropagation

2021 ◽  
pp. 195-200
Author(s):  
Heru Rahmat Wibawa Putra ◽  
Y Yuhandri
Keyword(s):  
Virus Disease ◽  
Imaging Techniques ◽  
Training Data ◽  
World Health ◽  
X Ray ◽  
Initial Stage ◽  
The World ◽  
Chest X Ray ◽  
Artificial Neural Network Ann ◽  
Health Organization

Corona Virus Disease 2019 (COVID-19) is an infectious respiratory disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2). This disease first appeared in Wuhan, China and spread throughout the world. COVID-19 has had a major impact on public health around the world. On March 9, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Early identification of people with COVID-19 can help limit the wider spread. One of the factors behind the rapid spread of the disease is the long clinical trial time. Rapid clinical testing is a challenge facing the spread of COVID-19. Most countries, including Indonesia, face the problem of lack of detection equipment and experts in diagnosing this disease. Chest X-Ray is one of the medical imaging techniques and also an alternative to identify the symptoms of pneumonia caused by COVID-19. This study aims to identify pneumonia caused by COVID-19 and other diseases based on Chest X-Ray. 107 Chest X-Ray images used as material for this study were obtained from the General Hospital of Ibnu Sina Padang Indonesia, which consisted of 27 images of pneumonia caused by COVID-19, 51 images with other diseases and 29 images of normal lungs. Then pre-processing is carried out as an initial stage and then feature extraction is carried out. Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm. In this study, 92 images were used as training data, and 15 images were used as test data. The results of calculations carried out using a network with a pattern of 16-100-100-100-2 obtained an accuracy value of 73%. The results of the identification prediction can be used as consideration in establishing a diagnosis of COVID-19 sufferers, but cannot be used as an absolute reference.

scholarly journals Identifikasi Penderita COVID-19 Berdasarkan Chest X-Ray Menggunakan Algoritma Jaringan Syaraf Tiruan Backpropagation

2021 ◽  
pp. 197-202
Author(s):  
Heru Rahmat Wibawa Putra ◽  
Y Yuhandri
Keyword(s):  
Virus Disease ◽  
Imaging Techniques ◽  
Training Data ◽  
World Health ◽  
X Ray ◽  
Initial Stage ◽  
The World ◽  
Chest X Ray ◽  
Artificial Neural Network Ann ◽  
Health Organization

Corona Virus Disease 2019 (COVID-19) is an infectious respiratory disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2). This disease first appeared in Wuhan, China and spread throughout the world. COVID-19 has had a major impact on public health around the world. On March 9, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Early identification of people with COVID-19 can help limit the wider spread. One of the factors behind the rapid spread of the disease is the long clinical trial time. Rapid clinical testing is a challenge facing the spread of COVID-19. Most countries, including Indonesia, face the problem of lack of detection equipment and experts in diagnosing this disease. Chest X-Ray is one of the medical imaging techniques and also an alternative to identify the symptoms of pneumonia caused by COVID-19. This study aims to identify pneumonia caused by COVID-19 and other diseases based on Chest X-Ray. 107 Chest X-Ray images used as material for this study were obtained from the General Hospital of Ibnu Sina Padang Indonesia, which consisted of 27 images of pneumonia caused by COVID-19, 51 images with other diseases and 29 images of normal lungs. Then pre-processing is carried out as an initial stage and then feature extraction is carried out. Furthermore, the learning and identification process is carried out using the Backpropagation Artificial Neural Network (ANN) algorithm. In this study, 92 images were used as training data, and 15 images were used as test data. The results of calculations carried out using a network with a pattern of 16-100-100-100-2 obtained an accuracy value of 73%. The results of the identification prediction can be used as consideration in establishing a diagnosis of COVID-19 sufferers, but cannot be used as an absolute reference.

scholarly journals Comparison of Measures adopted to combat COVID 19 Pandemic by different countries in WHO regions

2020 ◽  
Vol 32 (2 (Supp)) ◽  
pp. 288-299
Author(s):  
Shubha DB ◽  
Malathesh Undi ◽  
Rachana Annadani ◽  
Ayesha Siddique
Keyword(s):  
Virus Disease ◽  
Management Strategies ◽  
Vital Role ◽  
Control Measures ◽  
World Health ◽  
Contact Tracing ◽  
Limited Information ◽  
The World ◽  
The Difference ◽  
Health Organization

Since the emergence of Corona Virus Disease 19 (COVID 19) in China in December 2019, a lot of significant decisions have been taken by the World Health Organization (WHO) and several countries across the globe. As the world reels under the threat of rapid increase in the number of cases and is planning strategies with the limited information available on the virus, it is essential to learn from the experience of countries across the globe. Hence, we selected a few countries in five WHO regions based on their COVID 19 caseload, management strategies and outcome and compared some of the important measures taken by them to contain the spread of infection. Strategies like extensive testing and contact tracing, strict quarantine and isolation measures, Hospital preparedness, complete restriction of non-essential travel, strict border control measures and social distancing measures play a vital role in containment of the spread. All the countries faced the novel strain of virus and implemented similar strategies as per the guidance of WHO, but the extent of preparedness, swiftness with which the decisions were made and the scale of measures made the difference.

Can the Use of Bedside Lung Ultrasound Reduce Transmission Rates in The Case of The COVID-19 Patient? - A Narrative Review

POCUS Journal ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 26-28
Author(s):  
Sheena Bhimji-Hewitt MAppSc; DMS, CRGS, RDMS
Keyword(s):  
Health Care Workers ◽  
Lung Ultrasound ◽  
Virus Disease ◽  
Viral Disease ◽  
World Health ◽  
Rapid Review ◽  
Limited Information ◽  
Care Workers ◽  
The World ◽  
Health Organization

Novel Corona Virus Disease-19 (nCov-19, COVID-19) was recognised as a pandemic by the World Health Organization on March 11, 2020. As of June 14, 2020, this contagious viral disease has afflicted 188 out of 195 countries in the world with 7,893,700 confirmed cases and 432,922 global deaths. Canada has 98,787 people infected and 8,146 deaths. COVID-19 is thought to transmit through contact, droplets and aerosolization. A rapid review showed limited information on the benefits of conducting lung ultrasound (LUS) versus chest radiograph (CXR) or studies correlating lung ultrasound to chest computed Tomography (CT) in patients positive for Covid-19. The literature review confirmed that CT and LUS cannot diagnose this disease, but that both can help in the management and staging of this disease. There is no literature to prove that LUS at the bedside may be beneficial from the view of decreased transmission to other health care workers and bystanders due to reduced transit but comparing the transit pathway and contact leads one to propose that this would be so. Pregnant patients with COVID-19, young children and patients in the reproductive stage would also benefit from LUS since there is no radiation dose and the critical patient in distress will benefit from testing at the bedside.

scholarly journals Vital Role of Technology on Fighting Covid-19 Pandemic

2021 ◽  
pp. 114-117
Author(s):  
Dr. Jayendrasinh Jadav ◽  
Krishna Kulin Trivedi
Keyword(s):  
21St Century ◽  
World Wide ◽  
Virus Disease ◽  
Vital Role ◽  
World Health ◽  
Global Pandemic ◽  
Global Trend ◽  
The World ◽  
Health Organization

The whole fights against the corona virus disease which is an infectious respiratory disease which has high transmissibility and has no medical therapy or vaccine which has been declared as the pandemic by the world health organization popularly known in short as WHO and is a global pandemic. The 21st century is the digital age and digitalization is the global trend. Technology has played a vital role in fighting with the COVID-19 Pandemic. The sudden world-wide pandemic forced to imposed lockdown during which there is digital surge. This research paper focuses on the vital role of technology in fighting the COVID-19 Pandemic.

scholarly journals A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

2022 ◽  
Vol 17 (2) ◽  
pp. 1-27
Author(s):  
Rajeev Kumar Gupta ◽  
Nilesh Kunhare ◽  
Rajesh Kumar Pateriya ◽  
Nikhlesh Pathik
Keyword(s):  
Preliminary Test ◽  
Vital Role ◽  
World Health ◽  
Test Accuracy ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization ◽  
The Comparative Study

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

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 Fully-Connected Neural Network for COVID-19 Chest X-Ray Imaging Classification Using Hybrid Features

2020 ◽  
Author(s):  
Victor Hugo Viveiros ◽  
Rayanne Lima ◽  
Fernando Lucas Martins ◽  
Alessandra Coelho ◽  
Matheus Baffa
Keyword(s):  
Neural Network ◽  
The Other ◽  
World Health ◽  
Hybrid Features ◽  
X Ray ◽  
Validation Experiment ◽  
The World ◽  
Contact Tracking ◽  
Chest X Ray ◽  
Health Organization

Discovered on 31st December of 2019, the new Coronavirus has a high transmission capacity and was considered pandemic by the World Health Organization. In only six months is was able to spread all over the world and cause more than 600 thousand deaths. Early diagnosis is essential for governments to take public policies, such as social isolation, commerce control, and contact tracking. In order to make these actions, massive tests are required. On the other hand, diagnosis kits are expensive and not accessible to everyone. Medical imaging, such as thoracic x-ray and Computational Tomography (CT) has been used to visualize the lung and to verify at the first moment the presence of viral pneumonia. However, some countries have few radiologists specializing in chest x-ray analysis. The findings in the image are generally not so easy to see and can easily be confused with traditional pneumonia findings. For this reason, studies in Computer Vision are necessary, both to detect anomalies in imaging and to differentiate the other types of pneumonia. This paper addresses the initial results of a research, which developed an image classification methodology to differentiate x-ray images from sick patients, infected with Coronavirus, and healthy patients. The proposed method, based on the extraction and detection of patterns in texture and color features through a Deep Neural Network, obtained an average accuracy of 95% following a k-fold cross-validation experiment.

scholarly journals COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 315
Author(s):  
Soham Chattopadhyay ◽  
Arijit Dey ◽  
Pawan Kumar Singh ◽  
Zong Woo Geem ◽  
Ram Sarkar
Keyword(s):  
State Of The Art ◽  
Golden Ratio ◽  
World Health ◽  
Rt Pcr ◽  
X Ray ◽  
Global Pandemic ◽  
The World ◽  
Chest X Ray ◽  
Feature Selection Approach ◽  
Health Organization

The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.

scholarly journals SARS-CoV-2 infection and some controversies concerning therapy with chloroquine and hydroksychloroquine: case report and selected literature review

2020 ◽  
Vol 74 ◽  
pp. 348-353
Author(s):  
Hubert Ciepłucha ◽  
Brygida Knysz
Keyword(s):  
Case Report ◽  
Literature Review ◽  
Infected Patient ◽  
World Health ◽  
Efficacy And Safety ◽  
X Ray ◽  
Persistent Symptoms ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

Covid-19 is caused by a new virus and no effective therapy is available. The following article presents the case of a 47-year-old woman with SARS-CoV-2 infection. The infection was initially mild but because of exacerbation of the symptoms: cough, fever, headache, extreme weakness she was admitted to the hospital. The chest X-ray revealed pneumonia due to Covid-19, that is why CT was not done. Due to persistent symptoms of infection, therapy containing chloroquine and azithromycin was introduced, obtaining a very quick improvement in the condition of the infected patient. Because of ambiguous opinions of the efficacy of these two drugs in the therapy of SARS-CoV-2 infection, the authors wonder whether the improvement was either a result of the treatment with chloroquine and azithromycin or because of the natural Covid-19 course. The following part of the article briefly reviews research and world reports as well as problems connected with chloroquine and hydroxychloroquine therapy in patients with Covid-19. The current positions of the World Health Organization (WHO) and the Food and Drug Administration (FDA) in terms of the topic were also presented. It was also pointed out the way unprecedented before the therapy has been introduced based on several and variable report about the efficacy and safety of these drugs.

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