Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains
Many countries are severely affected by COVID-19, and various casualties have been reported. Most countries have implemented full and partial lockdowns to control COVID-19. Paramedical employee infections are always a threatening discovery. Front-line paramedical employees might initially be at risk...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/1296755 |
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| author | Yousef Alhwaiti Muhammad Hameed Siddiqi Madallah Alruwaili Ibrahim Alrashdi Saad Alanazi Muhammad Hasan Jamal |
| author_facet | Yousef Alhwaiti Muhammad Hameed Siddiqi Madallah Alruwaili Ibrahim Alrashdi Saad Alanazi Muhammad Hasan Jamal |
| author_sort | Yousef Alhwaiti |
| collection | DOAJ |
| description | Many countries are severely affected by COVID-19, and various casualties have been reported. Most countries have implemented full and partial lockdowns to control COVID-19. Paramedical employee infections are always a threatening discovery. Front-line paramedical employees might initially be at risk when observing and treating patients, who can contaminate them through respiratory secretions. If proper preventive measures are absent, front-line paramedical workers will be in danger of contamination and can become unintentional carriers to patients admitted in the hospital for other illnesses and treatments. Moreover, every country has limited testing capacity; therefore, a system is required which helps the doctor to directly check and analyze the patients’ blood structure. This study proposes a generalized adaptive deep learning model that helps the front-line paramedical employees to easily detect COVID-19 in different radiology domains. In this work, we designed a model using convolutional neural network in order to detect COVID-19 from X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) images. The proposed model has 27 layers (input, convolutional, max-pooling, dropout, flatten, dense, and output layers), which has been tested and validated on various radiology domains such as X-ray, CT, and MRI. For experiments, we utilized 70% of the dataset for training and 30% for testing against each dataset. The weighted average accuracies for the proposed model are 94%, 85%, and 86% on X-ray, CT, and MRI, respectively. The experiments show the significance of the model against state-of-the-art works. |
| format | Article |
| id | doaj-art-61d4c38cc22543c18609c550be9b3ce6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-61d4c38cc22543c18609c550be9b3ce62025-02-03T06:12:31ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/12967551296755Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology DomainsYousef Alhwaiti0Muhammad Hameed Siddiqi1Madallah Alruwaili2Ibrahim Alrashdi3Saad Alanazi4Muhammad Hasan Jamal5College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 2014, Saudi ArabiaCollege of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 2014, Saudi ArabiaCollege of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 2014, Saudi ArabiaCollege of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 2014, Saudi ArabiaCollege of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 2014, Saudi ArabiaDepartment of Computer Science, COMSATS University, Islamabad, Lahore Campus, PakistanMany countries are severely affected by COVID-19, and various casualties have been reported. Most countries have implemented full and partial lockdowns to control COVID-19. Paramedical employee infections are always a threatening discovery. Front-line paramedical employees might initially be at risk when observing and treating patients, who can contaminate them through respiratory secretions. If proper preventive measures are absent, front-line paramedical workers will be in danger of contamination and can become unintentional carriers to patients admitted in the hospital for other illnesses and treatments. Moreover, every country has limited testing capacity; therefore, a system is required which helps the doctor to directly check and analyze the patients’ blood structure. This study proposes a generalized adaptive deep learning model that helps the front-line paramedical employees to easily detect COVID-19 in different radiology domains. In this work, we designed a model using convolutional neural network in order to detect COVID-19 from X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) images. The proposed model has 27 layers (input, convolutional, max-pooling, dropout, flatten, dense, and output layers), which has been tested and validated on various radiology domains such as X-ray, CT, and MRI. For experiments, we utilized 70% of the dataset for training and 30% for testing against each dataset. The weighted average accuracies for the proposed model are 94%, 85%, and 86% on X-ray, CT, and MRI, respectively. The experiments show the significance of the model against state-of-the-art works.http://dx.doi.org/10.1155/2021/1296755 |
| spellingShingle | Yousef Alhwaiti Muhammad Hameed Siddiqi Madallah Alruwaili Ibrahim Alrashdi Saad Alanazi Muhammad Hasan Jamal Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains Complexity |
| title | Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains |
| title_full | Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains |
| title_fullStr | Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains |
| title_full_unstemmed | Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains |
| title_short | Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains |
| title_sort | diagnosis of covid 19 using a deep learning model in various radiology domains |
| url | http://dx.doi.org/10.1155/2021/1296755 |
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