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Considering the prevailing scenario of COVID-19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (rRT-PCR) test. However, the chest radiological (X-ray) imaging can be used as an alternate method to rRT-PCR test, and early COVID-19 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)-based analytical framework is developed for automatic detection of COVID-19 using chest X-ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID-19. The experimental results pose the proposed framework as a potential candidate for COVID-19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four-class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID-19 detection along with other types of pneumonia. K E Y W O R D Schest X-ray images, coronavirus, machine learning methods, pneumonia | INTRODUCTIONIn December 2019, local outbreak of a strange kind of pneumonia due to an unknown source was reported in the city of Wuhan, China. 1 The source of the disease was soon discovered to be a new strain of "Coronavirus" (CoV) termed as "Severe Acute Respiratory Syndrome Coronavirus 2" (SARS-CoV-2) by the international committee on taxonomy of viruses. The disease caused by the virus was named Coronavirus Disease-2019 (COVID-19) by World Health Organisation (WHO) in February 2020. COVID-19 is a highly contagious, upper respiratory syndrome with more than 7 million confirmed infections in about 191 countries as of June 16, 2020. 2 Presently, the disease has been held responsible for causing over a million deaths all across the globe, with the highest trolls in the countries like United States,
Considering the prevailing scenario of COVID-19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (rRT-PCR) test. However, the chest radiological (X-ray) imaging can be used as an alternate method to rRT-PCR test, and early COVID-19 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)-based analytical framework is developed for automatic detection of COVID-19 using chest X-ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID-19. The experimental results pose the proposed framework as a potential candidate for COVID-19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four-class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID-19 detection along with other types of pneumonia. K E Y W O R D Schest X-ray images, coronavirus, machine learning methods, pneumonia | INTRODUCTIONIn December 2019, local outbreak of a strange kind of pneumonia due to an unknown source was reported in the city of Wuhan, China. 1 The source of the disease was soon discovered to be a new strain of "Coronavirus" (CoV) termed as "Severe Acute Respiratory Syndrome Coronavirus 2" (SARS-CoV-2) by the international committee on taxonomy of viruses. The disease caused by the virus was named Coronavirus Disease-2019 (COVID-19) by World Health Organisation (WHO) in February 2020. COVID-19 is a highly contagious, upper respiratory syndrome with more than 7 million confirmed infections in about 191 countries as of June 16, 2020. 2 Presently, the disease has been held responsible for causing over a million deaths all across the globe, with the highest trolls in the countries like United States,
No abstract
The outbreak of coronavirus disease 2019 (COVID-19) continues to have a catastrophic impact on the living standard of people worldwide. To fight against COVID-19, many countries are using a combination of containment and mitigation activities. Effective screening of contaminated patients is a critical step in the battle against COVID-19. During the early medical examination, it was observed that patient having abnormalities in chest radiography images shows the symptoms of COVID-19 infection. Motivated by this, in this article, we proposed a unique framework to diagnose the COVID-19 infection. Here, we removed the fully connected layers of an already proven model VGG-16 and placed a new simplified fully connected layer set that is initialized with some random weights on top of this deep convolutional neural network, which has already learned discriminative features, namely, edges, colors, geometric changes,shapes, and objects. To avoid the risk of destroying the rich features, we warm up our FC head by seizing all layers in the body of our network and then unfreeze all the layers in the network body to be fine-tuned.The suggested classification model achieved an accuracy of 97.12% with 99.2% sensitivity and 99.6% specificity for COVID-19 identification. This classification model is superior to the other classification model used to classify COVID-19 infected patients.
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