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AuthorSakib, Sadman
AuthorTazrin, Tahrat
AuthorFouda, Mostafa M.
AuthorFadlullah, Zubair Md
AuthorGuizani, Mohsen
Available date2022-12-22T08:46:13Z
Publication Date2020-01-01
Publication NameIEEE Access
Identifierhttp://dx.doi.org/10.1109/ACCESS.2020.3025010
CitationSakib, S., Tazrin, T., Fouda, M. M., Fadlullah, Z. M., & Guizani, M. (2020). DL-CRC: deep learning-based chest radiograph classification for COVID-19 detection: a novel approach. Ieee Access, 8, 171575-171589.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85101867177&origin=inward
URIhttp://hdl.handle.net/10576/37546
AbstractWith 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.
SponsorThis work was supported in part by the MITACS Accelerate under Grant IT18879, and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Discovery Grant RGPIN-2020-06260.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectConvolutional neural network (CNN)
COVID-19
Deep learning
Generative adversarial network (GAN)
Pneumonia
TitleDL-CRC: Deep learning-based chest radiograph classification for covid-19 detection: A novel approach
TypeArticle
Pagination171575-171589
Volume Number8
dc.accessType Open Access


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