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. |
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| 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] |
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| Database: | Engineering Source |
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