Detection of COVID-19 Based on Chest X-rays Using Deep Learning
The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different D...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/2/343 |
| _version_ | 1797479643170734080 |
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| author | Walaa Gouda Maram Almurafeh Mamoona Humayun Noor Zaman Jhanjhi |
| author_facet | Walaa Gouda Maram Almurafeh Mamoona Humayun Noor Zaman Jhanjhi |
| author_sort | Walaa Gouda |
| collection | DOAJ |
| description | The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained. |
| first_indexed | 2024-03-09T21:48:44Z |
| format | Article |
| id | doaj.art-3aaf88f7d37d40eeabe087b5cc1f8a30 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T21:48:44Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-3aaf88f7d37d40eeabe087b5cc1f8a302023-11-23T20:10:21ZengMDPI AGHealthcare2227-90322022-02-0110234310.3390/healthcare10020343Detection of COVID-19 Based on Chest X-rays Using Deep LearningWalaa Gouda0Maram Almurafeh1Mamoona Humayun2Noor Zaman Jhanjhi3Department of Computer Engineering and Network, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi ArabiaSchool of Computer Science and Engineering (SCE), Taylor’s University, Subang Jaya 47500, Selangor, MalaysiaThe coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained.https://www.mdpi.com/2227-9032/10/2/343COVID-19chest X-raypneumoniadeep transfer learningneural network (NN) |
| spellingShingle | Walaa Gouda Maram Almurafeh Mamoona Humayun Noor Zaman Jhanjhi Detection of COVID-19 Based on Chest X-rays Using Deep Learning Healthcare COVID-19 chest X-ray pneumonia deep transfer learning neural network (NN) |
| title | Detection of COVID-19 Based on Chest X-rays Using Deep Learning |
| title_full | Detection of COVID-19 Based on Chest X-rays Using Deep Learning |
| title_fullStr | Detection of COVID-19 Based on Chest X-rays Using Deep Learning |
| title_full_unstemmed | Detection of COVID-19 Based on Chest X-rays Using Deep Learning |
| title_short | Detection of COVID-19 Based on Chest X-rays Using Deep Learning |
| title_sort | detection of covid 19 based on chest x rays using deep learning |
| topic | COVID-19 chest X-ray pneumonia deep transfer learning neural network (NN) |
| url | https://www.mdpi.com/2227-9032/10/2/343 |
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