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A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

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Title: A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.
Authors: Subramaniam, Kavitha1 (AUTHOR), Palanisamy, Natesan1 (AUTHOR), Sinnaswamy, Renugadevi Ammapalayam2 (AUTHOR), Muthusamy, Suresh2 (AUTHOR) infostosuresh@gmail.com, Mishra, Om Prava3 (AUTHOR), Loganathan, Ashok Kumar4 (AUTHOR), Ramamoorthi, Ponarun5 (AUTHOR), Gnanakkan, Christober Asir Rajan Charles6 (AUTHOR), Thangavel, Gunasekaran7 (AUTHOR), Sundararajan, Suma Christal Mary8 (AUTHOR)
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Oct2023, Vol. 27 Issue 19, p14219-14240. 22p.
Subjects: Deep learning, Convolutional neural networks, COVID-19, X-ray imaging, Respiratory diseases, SARS virus
Abstract: COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.
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  Data: <searchLink fieldCode="AR" term="%22Subramaniam%2C+Kavitha%22">Subramaniam, Kavitha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Palanisamy%2C+Natesan%22">Palanisamy, Natesan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sinnaswamy%2C+Renugadevi+Ammapalayam%22">Sinnaswamy, Renugadevi Ammapalayam</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Muthusamy%2C+Suresh%22">Muthusamy, Suresh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> infostosuresh@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mishra%2C+Om+Prava%22">Mishra, Om Prava</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Loganathan%2C+Ashok+Kumar%22">Loganathan, Ashok Kumar</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramamoorthi%2C+Ponarun%22">Ramamoorthi, Ponarun</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gnanakkan%2C+Christober+Asir+Rajan+Charles%22">Gnanakkan, Christober Asir Rajan Charles</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thangavel%2C+Gunasekaran%22">Thangavel, Gunasekaran</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sundararajan%2C+Suma+Christal+Mary%22">Sundararajan, Suma Christal Mary</searchLink><relatesTo>8</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+imaging%22">X-ray imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Respiratory+diseases%22">Respiratory diseases</searchLink><br /><searchLink fieldCode="DE" term="%22SARS+virus%22">SARS virus</searchLink>
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  Data: COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=170407294
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      – SubjectFull: COVID-19
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