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 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.

scholarly journals A Review on Automatic Covid-19 Lung Infection Detection from Different Imaging Techniques

2021 ◽  
Vol 9 (VII) ◽  
pp. 3850-3856
Author(s):  
Sangeeta Singh
Keyword(s):  
World Health Organization ◽  
Virus Disease ◽  
Imaging Techniques ◽  
World Health ◽  
The Novel ◽  
Corona Virus ◽  
The World ◽  
Health Organization ◽  
High Level ◽  
Precision Rate

Corona Virus Disease-2019 commonly known as COVID-19 which has been defined by the Novel Corona Virus. It is a family of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and was first detected during respiratory outbreak. It was first reported to the World Health Organization on December 31, 2019. On January 30, 2020, the World Health Organization declared the COVID-19 eruption a global health emergency. As of 27-May-2021 169,095,283 confirmed cases have been reported in the world and 2, 73, 67, 935 cases in India. It is required to identify the infection with high precision rate but there are lots of deficiency in the diagnosing system that may resulted false alarm rate. Initially it could be detected through throat saliva but now it can also be identified thought the impairment in lungs from computerized tomographical imaging technique. This paper reviewed various researches over COVID-19 diagnosis approach as well as the syndrome in respiratory organs. There are so many imaging techniques through which lungs impairments can be detected that may diagnose COVID-19 with high level of accuracy. CT scan image is the best alternative for diagnosing COVID-19.

scholarly journals Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images

2020 ◽  
pp. 119-130
Author(s):  
Shadman Q. Salih ◽  
Hawre Kh. Abdulla ◽  
Zanear Sh. Ahmed ◽  
Nigar M. Shafiq Surameery ◽  
Rasper Dh. Rashid
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Imaging Techniques ◽  
Convolution Neural Network ◽  
World Health ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization ◽  
Fully Connected ◽  
The City

First outbreak of COVID-19 was in the city of Wuhan in China in Dec.2019 and then it becomes a pandemic disease all around the world. World Health Organization (WHO) confirmed more than 5.5 million cases and 341,155 deaths from the disease till the time of writing this paper. This new worldwide disease forced researchers to make more precise way to diagnose COVID-19. In the last decade, medical imaging techniques show its efficiency in helping radiologists to detect and diagnose the diseases. Deep learning and transfer learning algorithms are good techniques to detect disease from different image source types such as X-Ray and CT scan images. In this work we used a deep learning technique based on Convolution Neural Network (CNN) to detect and diagnose COVID-19 disease using Chest X-ray images.  Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. The used chest X-ray images are gathered from two COVID-19 X-ray image datasets and one dataset includes large number of normal and pneumonia X-ray images. With the proposed models we obtained the same or even better result than the original AlexNet with having a smaller number of neurons in the second fully connected layer.

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.

COVID 19: REALS AND CHALLENGES AGAINST DOCTORS

2020 ◽  
Vol 2 (1) ◽  
pp. 65-87
Author(s):  
Zen Ahmad
Keyword(s):  
Virus Disease ◽  
Clinical Manifestations ◽  
World Health ◽  
Unknown Etiology ◽  
Wuhan City ◽  
Traditional Markets ◽  
Pneumonia Case ◽  
The World ◽  
New Type ◽  
Health Organization

Corona Virus Disease (Covid-19) is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which was discovered in December 2019 in China. This disease can cause clinical manifestations in the airway, lung and systemic. The World Health Organization (WHO) representative of China reported a pneumonia case with unknown etiology in Wuhan City, Hubei Province, China on December 31, 2019. The cause was identified as a new type of coronavirus on January 7, 2020 with an estimated source of the virus from traditional markets (seafood market). ) Wuhan city

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

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