A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches

Waleed Al Shehri, Jameel Almalki, Rashid Mehmood, Khalid Alsaif, Saeed M. Alshahrani (), Najlaa Jannah and Someah Alangari
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Waleed Al Shehri: Department of Computer Science, College of Computer in Al-Lith, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Jameel Almalki: Department of Computer Science, College of Computer in Al-Lith, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Rashid Mehmood: High Performance Computing Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Khalid Alsaif: Department of Computer Science, King Abdulaziz University, Jeddah 22254, Saudi Arabia
Saeed M. Alshahrani: Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
Najlaa Jannah: Department of Computer Science, College of Computer in Al-Lith, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Someah Alangari: Department of Computer Science, College of Science and Humanities, Dawadmi, Shaqra University, Shaqra 11961, Saudi Arabia

Sustainability, 2022, vol. 14, issue 19, 1-12

Abstract: The COVID-19 pandemic affects individuals in many ways and has spread worldwide. Current methods of COVID-19 detection are based on physicians analyzing the patient’s symptoms. Machine learning with deep learning approaches applied to image processing techniques also plays a role in identifying COVID-19 from minor symptoms. The problem is that such models do not provide high performance, which impacts timely decision-making. Early disease detection in many places is limited due to the lack of expensive resources. This study employed pre-implemented instances of a convolutional neural network and Darknet to process CT scans and X-ray images. Results show that the proposed new models outperformed the state-of-the-art methods by approximately 10% in accuracy. The results will help physicians and the health care system make preemptive decisions regarding patient health. The current approach might be used jointly with existing health care systems to detect and monitor cases of COVID-19 disease quickly.

Keywords: COVID-19 detection; CNN; Darknet; CT-scans; X-rays; COVID-19 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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