Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach
Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded...
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| Format: | Article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://eprints.utm.my/94589/1/AzlanMohd2021_DetectionofCovid19inChestXRay.pdf |
| _version_ | 1796865852348301312 |
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| author | Awan, Mazhar Javed Bilal, Muhammad Haseeb Yasin, Awais Nobanee, Haitham Khan, Nabeel Sabir Mohd. Zain, Azlan |
| author_facet | Awan, Mazhar Javed Bilal, Muhammad Haseeb Yasin, Awais Nobanee, Haitham Khan, Nabeel Sabir Mohd. Zain, Azlan |
| author_sort | Awan, Mazhar Javed |
| collection | ePrints |
| description | Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively. |
| first_indexed | 2024-03-05T21:03:24Z |
| format | Article |
| id | utm.eprints-94589 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:03:24Z |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-945892022-03-31T15:48:13Z http://eprints.utm.my/94589/ Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach Awan, Mazhar Javed Bilal, Muhammad Haseeb Yasin, Awais Nobanee, Haitham Khan, Nabeel Sabir Mohd. Zain, Azlan QA75 Electronic computers. Computer science Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively. MDPI 2021-10-01 Article PeerReviewed application/pdf en http://eprints.utm.my/94589/1/AzlanMohd2021_DetectionofCovid19inChestXRay.pdf Awan, Mazhar Javed and Bilal, Muhammad Haseeb and Yasin, Awais and Nobanee, Haitham and Khan, Nabeel Sabir and Mohd. Zain, Azlan (2021) Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach. International Journal of Environmental Research and Public Health, 18 (19). pp. 1-16. ISSN 1661-7827 http://dx.doi.org/10.3390/ijerph181910147 DOI:10.3390/ijerph181910147 |
| spellingShingle | QA75 Electronic computers. Computer science Awan, Mazhar Javed Bilal, Muhammad Haseeb Yasin, Awais Nobanee, Haitham Khan, Nabeel Sabir Mohd. Zain, Azlan Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach |
| title | Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach |
| title_full | Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach |
| title_fullStr | Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach |
| title_full_unstemmed | Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach |
| title_short | Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach |
| title_sort | detection of covid 19 in chest x ray images a big data enabled deep learning approach |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/94589/1/AzlanMohd2021_DetectionofCovid19inChestXRay.pdf |
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