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Article

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

1
Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
2
School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan
3
Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
4
National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh 12391, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editors: Mukesh Prasad, Jian Cao, Chintan Bhatt, Monowar H. Bhuyan and Behnaz Ghoraani
Sensors 2022, 22(2), 669; https://doi.org/10.3390/s22020669
Received: 8 November 2021 / Revised: 11 January 2022 / Accepted: 12 January 2022 / Published: 16 January 2022
The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis. View Full-Text
Keywords: COVID-19; pneumonia; chest X-ray (CXR); deep learning (DL); clinical data COVID-19; pneumonia; chest X-ray (CXR); deep learning (DL); clinical data
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MDPI and ACS Style

Khan, I.U.; Aslam, N.; Anwar, T.; Alsaif, H.S.; Chrouf, S.M.B.; Alzahrani, N.A.; Alamoudi, F.A.; Kamaleldin, M.M.A.; Awary, K.B. Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray. Sensors 2022, 22, 669. https://doi.org/10.3390/s22020669

AMA Style

Khan IU, Aslam N, Anwar T, Alsaif HS, Chrouf SMB, Alzahrani NA, Alamoudi FA, Kamaleldin MMA, Awary KB. Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray. Sensors. 2022; 22(2):669. https://doi.org/10.3390/s22020669

Chicago/Turabian Style

Khan, Irfan U., Nida Aslam, Talha Anwar, Hind S. Alsaif, Sara M.B. Chrouf, Norah A. Alzahrani, Fatimah A. Alamoudi, Mariam M.A. Kamaleldin, and Khaled B. Awary. 2022. "Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray" Sensors 22, no. 2: 669. https://doi.org/10.3390/s22020669

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