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Deep GRU-CNN model for COVID-19 detection from chest X-rays data
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Shah, Pir Masoom, Ullah, Faizan, Shah, Dilawar, Gani, Abdullah, Maple, Carsten, Wang, Yulin, Shahid, A., Abrar, Mohammad and Islam, Saif ul (2021) Deep GRU-CNN model for COVID-19 detection from chest X-rays data. IEEE Access, 10 . pp. 35094-35105. doi:10.1109/ACCESS.2021.3077592
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Official URL: http://dx.doi.org/10.1109/ACCESS.2021.3077592
Abstract
In the current era, big data is growing exponentially due to advancements in smart devices. Data scientists apply varied learning-based techniques to identify the underlying patterns in the big medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science due to rapid population growth. It reduces the mortality rate by diagnosing the disease correctly and early enough. The novel virus disease COVID-19 has spread all over the world and is affecting millions of people. Many countries are facing a shortage of test kits, vaccines, and other resources due to substantial growth in COVID-19 cases. In order to accelerate the testing process, scientists around the world have sought to create revolutionary novel alternative methods for the detection of the deadly virus. In this paper, we have proposed a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) for diagnosing the virus from chest X-rays (CXRs). In the proposed model, CNN is used to extract features, and GRU is used as a classifier. The model has been trained on 424 CXRs images with 3 (COIVD-19, Pneumonia, and Normal) classes. The proposed model achieved encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 patients using X-ray scans. Such indications can pave the ways to mitigate the deadly disease. We believe that this model can be an effective tool for medical practitioners for the early diagnosis of coronavirus from CXRs.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > Q Science (General) R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | COVID-19 (Disease) , COVID-19 (Disease) -- Epidemiology -- Data processing, Medical records -- Data processing, Artificial intelligence -- Medical applications, Machine learning, Neural networks (Computer science), Chest -- Radiography, X-rays | |||||||||
| Journal or Publication Title: | IEEE Access | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 2169-3536 | |||||||||
| Official Date: | 5 May 2021 | |||||||||
| Dates: |
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| Volume: | 10 | |||||||||
| Page Range: | pp. 35094-35105 | |||||||||
| DOI: | 10.1109/ACCESS.2021.3077592 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| RIOXX Funder/Project Grant: |
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