Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

Muhammad Irfan, Muhammad Aksam Iftikhar, Sana Yasin, Zaghum Umar, Tariq Ali, Shafiq Hussain, Sarah Bukhari, Abdullah Saeed Alwadie, Saifur Rahman, Adam Glowacz and Faisal Althobiani
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Muhammad Irfan: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Muhammad Aksam Iftikhar: Department of Computer Science, Lahore Campus, COMSATS University Islamabad, Lahore 54000, Pakistan
Sana Yasin: Department of Computer Science, University of OKara, Okara 56130, Pakistan
Tariq Ali: Computer Science Department, Sahiwal Campus, COMSATS University Islamabad, Sahiwal 57000, Pakistan
Shafiq Hussain: Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan
Sarah Bukhari: Department of Computer Science, National Fertilizer Corporation Institute of Engineering and Technology, Multan 60000, Pakistan
Abdullah Saeed Alwadie: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Saifur Rahman: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Adam Glowacz: Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland
Faisal Althobiani: Faculty of Maritime Studies, King Abdulaziz University, Jeddah 21577, Saudi Arabia

IJERPH, 2021, vol. 18, issue 6, 1-14

Abstract: COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.

Keywords: hybrid deep neural network (HDNNs); computed tomography (CT-scan); long short-term memory (LSTM); COVID-19 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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