A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches
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 COVI...
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| Veröffentlicht in: | Sustainability 2022-09, Vol.14 (19), p.12222 |
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| creator | Al Shehri, Waleed Almalki, Jameel Mehmood, Rashid Alsaif, Khalid Alshahrani, Saeed M Jannah, Najlaa Alangari, Someah |
| description | 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. |
| doi_str_mv | 10.3390/su141912222 |
| format | Article |
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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.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su141912222</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Artificial intelligence ; Big Data ; Blockchain ; Computed tomography ; Coronaviruses ; COVID-19 ; COVID-19 vaccines ; Data analysis ; Datasets ; Decision making ; Deep learning ; Disease detection ; Health aspects ; Health care ; Image processing ; Lung diseases ; Machine learning ; Medical imaging ; Neural networks ; Pandemics ; Patients ; Physicians ; Preempting ; Severe acute respiratory syndrome coronavirus 2 ; Signs and symptoms ; Sustainability</subject><ispartof>Sustainability, 2022-09, Vol.14 (19), p.12222</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Algorithms Artificial intelligence Big Data Blockchain Computed tomography Coronaviruses COVID-19 COVID-19 vaccines Data analysis Datasets Decision making Deep learning Disease detection Health aspects Health care Image processing Lung diseases Machine learning Medical imaging Neural networks Pandemics Patients Physicians Preempting Severe acute respiratory syndrome coronavirus 2 Signs and symptoms Sustainability |
| title | A Novel COVID-19 Detection Technique Using Deep Learning Based Approaches |
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