Deep learning model for detection of COVID-19 utilizing the chest X-ray images
The COVID-19 pandemic has caused more than 200 million infected cases and 4 million deaths across the world. The pandemic has triggered a massive epidemic, with a significant effect on the health and lives of many people worldwide. Early detection of this disease is very important for maintaining so...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2022.2079221 |
| _version_ | 1797703265952989184 |
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| author | Shahanaz Abdul Gafoor Niranjana Sampathila Madhushankara M Swathi K S |
| author_facet | Shahanaz Abdul Gafoor Niranjana Sampathila Madhushankara M Swathi K S |
| author_sort | Shahanaz Abdul Gafoor |
| collection | DOAJ |
| description | The COVID-19 pandemic has caused more than 200 million infected cases and 4 million deaths across the world. The pandemic has triggered a massive epidemic, with a significant effect on the health and lives of many people worldwide. Early detection of this disease is very important for maintaining social well-being. Generally, the RT-PCR test is a diagnosis method used for the detection of the COVID-19, yet it is not the only reliable diagnostic tool. In this study, we discuss the image-based modalities for the detection of coronavirus utilizing Deep Learning methodology, which is one of the most innovative technologies today and has proven to be an efficient solution for a number of medical conditions. Coronavirus affects the respiratory tract of individuals. One of the best ways is to identify this disease from chest radiography images. Early research demonstrated unique anomalies in chest radiographs of covid-positive patients. By using Deep Learning Multi-layered networks, we classified the chest images as covid positive or negative. The proposed model uses the dataset of patients infected with Coronavirus, in which the radiologist indicated multilobar involvements in the chest X-rays. A total of 6500 images have been considered for the study. The convolutional network (CNN) model was trained and a validation accuracy of 94% is obtained. |
| first_indexed | 2024-03-12T05:03:03Z |
| format | Article |
| id | doaj.art-b645620045df4bbeb7bf2d187c369a02 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T05:03:03Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-b645620045df4bbeb7bf2d187c369a022023-09-03T09:08:04ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2022.2079221Deep learning model for detection of COVID-19 utilizing the chest X-ray imagesShahanaz Abdul Gafoor0Niranjana Sampathila1Madhushankara M2Swathi K S3Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, IndiaManipal School of Information Sciences, Manipal Academy of Higher Education (MAHE), Manipal, IndiaManipal Institute of Management, Manipal Academy of Higher Education (MAHE), Manipal, IndiaThe COVID-19 pandemic has caused more than 200 million infected cases and 4 million deaths across the world. The pandemic has triggered a massive epidemic, with a significant effect on the health and lives of many people worldwide. Early detection of this disease is very important for maintaining social well-being. Generally, the RT-PCR test is a diagnosis method used for the detection of the COVID-19, yet it is not the only reliable diagnostic tool. In this study, we discuss the image-based modalities for the detection of coronavirus utilizing Deep Learning methodology, which is one of the most innovative technologies today and has proven to be an efficient solution for a number of medical conditions. Coronavirus affects the respiratory tract of individuals. One of the best ways is to identify this disease from chest radiography images. Early research demonstrated unique anomalies in chest radiographs of covid-positive patients. By using Deep Learning Multi-layered networks, we classified the chest images as covid positive or negative. The proposed model uses the dataset of patients infected with Coronavirus, in which the radiologist indicated multilobar involvements in the chest X-rays. A total of 6500 images have been considered for the study. The convolutional network (CNN) model was trained and a validation accuracy of 94% is obtained.https://www.tandfonline.com/doi/10.1080/23311916.2022.2079221deep learningconvolutional neural networks (CNN)chest X-rayCOVID-19pandemic |
| spellingShingle | Shahanaz Abdul Gafoor Niranjana Sampathila Madhushankara M Swathi K S Deep learning model for detection of COVID-19 utilizing the chest X-ray images Cogent Engineering deep learning convolutional neural networks (CNN) chest X-ray COVID-19 pandemic |
| title | Deep learning model for detection of COVID-19 utilizing the chest X-ray images |
| title_full | Deep learning model for detection of COVID-19 utilizing the chest X-ray images |
| title_fullStr | Deep learning model for detection of COVID-19 utilizing the chest X-ray images |
| title_full_unstemmed | Deep learning model for detection of COVID-19 utilizing the chest X-ray images |
| title_short | Deep learning model for detection of COVID-19 utilizing the chest X-ray images |
| title_sort | deep learning model for detection of covid 19 utilizing the chest x ray images |
| topic | deep learning convolutional neural networks (CNN) chest X-ray COVID-19 pandemic |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2022.2079221 |
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