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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

by
Irfan Ullah Khan
1,
Nida Aslam
1,*,
Talha Anwar
2,
Hind S. Alsaif
3,
Sara Mhd. Bachar Chrouf
1,
Norah A. Alzahrani
1,4,
Fatimah Ahmed Alamoudi
1,
Mariam Moataz Aly Kamaleldin
1 and
Khaled Bassam Awary
3
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.
Sensors 2022, 22(2), 669; https://doi.org/10.3390/s22020669
Submission received: 8 November 2021 / Revised: 11 January 2022 / Accepted: 12 January 2022 / Published: 16 January 2022
(This article belongs to the Special Issue Advanced Machine Learning Techniques for Biomedical Imaging Sensing and Healthcare Applications)
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Abstract

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.
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

Share and Cite

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 Ullah, Nida Aslam, Talha Anwar, Hind S. Alsaif, Sara Mhd. Bachar Chrouf, Norah A. Alzahrani, Fatimah Ahmed Alamoudi, Mariam Moataz Aly Kamaleldin, and Khaled Bassam 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

APA 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. (2022). Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray. Sensors, 22(2), 669. https://doi.org/10.3390/s22020669

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