Medical image-based detection of COVID-19 using Deep Convolution Neural Networks
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect CO...
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| Published in: | Multimedia systems Vol. 29; no. 3; pp. 1729 - 1738 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer Berlin Heidelberg
01.06.2023
Springer Nature B.V |
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| ISSN: | 0942-4962, 1432-1882 |
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| Abstract | The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. |
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| AbstractList | The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. |
| Author | Muhammad, Ghulam Bhatia, Ujwal Jhanjhi, N. Z. Masud, Mehedi Gaur, Loveleen |
| Author_xml | – sequence: 1 givenname: Loveleen surname: Gaur fullname: Gaur, Loveleen organization: Amity International Business School, Amity University – sequence: 2 givenname: Ujwal surname: Bhatia fullname: Bhatia, Ujwal organization: Amity International Business School, Amity University – sequence: 3 givenname: N. Z. surname: Jhanjhi fullname: Jhanjhi, N. Z. organization: School of Computer Science and Engineering SCE, Taylor’s University – sequence: 4 givenname: Ghulam orcidid: 0000-0002-9781-3969 surname: Muhammad fullname: Muhammad, Ghulam email: ghulam@ksu.edu.sa organization: Research Chair of Pervasive and Mobile Computing, King Saud University, Computer Engineering Department, College of Computer and Information Sciences, King Saud University – sequence: 5 givenname: Mehedi surname: Masud fullname: Masud, Mehedi organization: Department of Computer Science, College of Computers and Information Technology, Taif University |
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| Keywords | COVID-19 Deep learning Computer vision Chest X-rays Transfer learning Deep CNN |
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| License | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
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| Snippet | The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare... |
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| SubjectTerms | Accuracy Algorithms Applications programs Artificial neural networks Business analytics Classification Comparative analysis Computer Communication Networks Computer Graphics Computer Science Computer vision Convolution COVID-19 Cryptology Data Storage Representation Datasets Decision making Deep learning Health care facilities Lung diseases Machine learning Medical diagnosis Medical imaging Medical research Mobile computing Multimedia Multimedia Information Systems Neural networks Operating Systems Performance measurement Pneumonia Role of Deep Learning Models & Analytics in Industrial Multimedia Environment Special Issue Paper X-rays |
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| Title | Medical image-based detection of COVID-19 using Deep Convolution Neural Networks |
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