DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach
With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely avai...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9199819/ |
| _version_ | 1818833586539724800 |
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| author | Sadman Sakib Tahrat Tazrin Mostafa M. Fouda Zubair Md. Fadlullah Mohsen Guizani |
| author_facet | Sadman Sakib Tahrat Tazrin Mostafa M. Fouda Zubair Md. Fadlullah Mohsen Guizani |
| author_sort | Sadman Sakib |
| collection | DOAJ |
| description | With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities. |
| first_indexed | 2024-12-19T02:21:17Z |
| format | Article |
| id | doaj.art-a11108e21df042e7b8b6bf022a8f63e7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T02:21:17Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a11108e21df042e7b8b6bf022a8f63e72022-12-21T20:40:12ZengIEEEIEEE Access2169-35362020-01-01817157517158910.1109/ACCESS.2020.30250109199819DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel ApproachSadman Sakib0https://orcid.org/0000-0003-1922-7531Tahrat Tazrin1https://orcid.org/0000-0002-4123-8854Mostafa M. Fouda2https://orcid.org/0000-0003-1790-8640Zubair Md. Fadlullah3https://orcid.org/0000-0002-4785-2425Mohsen Guizani4https://orcid.org/0000-0002-8972-8094Department of Computer Science, Lakehead University, Thunder Bay, CanadaDepartment of Computer Science, Lakehead University, Thunder Bay, CanadaDepartment of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID, USADepartment of Computer Science, Lakehead University, Thunder Bay, CanadaDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarWith the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.https://ieeexplore.ieee.org/document/9199819/COVID-19convolutional neural network (CNN)deep learninggenerative adversarial network (GAN)pneumonia |
| spellingShingle | Sadman Sakib Tahrat Tazrin Mostafa M. Fouda Zubair Md. Fadlullah Mohsen Guizani DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach IEEE Access COVID-19 convolutional neural network (CNN) deep learning generative adversarial network (GAN) pneumonia |
| title | DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach |
| title_full | DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach |
| title_fullStr | DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach |
| title_full_unstemmed | DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach |
| title_short | DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach |
| title_sort | dl crc deep learning based chest radiograph classification for covid 19 detection a novel approach |
| topic | COVID-19 convolutional neural network (CNN) deep learning generative adversarial network (GAN) pneumonia |
| url | https://ieeexplore.ieee.org/document/9199819/ |
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