Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs
Abstract The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47038-3 |
| _version_ | 1797363413002747904 |
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| author | Ahmad MohdAziz Hussein Abdulrauf Garba Sharifai Osama Moh’d Alia Laith Abualigah Khaled H. Almotairi Sohaib K. M. Abujayyab Amir H. Gandomi |
| author_facet | Ahmad MohdAziz Hussein Abdulrauf Garba Sharifai Osama Moh’d Alia Laith Abualigah Khaled H. Almotairi Sohaib K. M. Abujayyab Amir H. Gandomi |
| author_sort | Ahmad MohdAziz Hussein |
| collection | DOAJ |
| description | Abstract The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples. |
| first_indexed | 2024-03-08T16:21:01Z |
| format | Article |
| id | doaj.art-068c03685b7a4ce1a5fe51573545b63c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T16:21:01Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-068c03685b7a4ce1a5fe51573545b63c2024-01-07T12:21:27ZengNature PortfolioScientific Reports2045-23222024-01-0114111810.1038/s41598-023-47038-3Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographsAhmad MohdAziz Hussein0Abdulrauf Garba Sharifai1Osama Moh’d Alia2Laith Abualigah3Khaled H. Almotairi4Sohaib K. M. Abujayyab5Amir H. Gandomi6Department of Computer Science, Faculty of Information Technology, Middle East UniversityDepartment of Computer Sciences, Yusuf Maitama Sule UniversityDepartment of Computer Science, Faculty of Computes and Information Technology, University of TabukComputer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt UniversityComputer Engineering Department, Computer and Information Systems College, Umm Al-Qura UniversityInternational College for Engineering and ManagementFaculty of Engineering and Information Technology, University of Technology SydneyAbstract The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.https://doi.org/10.1038/s41598-023-47038-3 |
| spellingShingle | Ahmad MohdAziz Hussein Abdulrauf Garba Sharifai Osama Moh’d Alia Laith Abualigah Khaled H. Almotairi Sohaib K. M. Abujayyab Amir H. Gandomi Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs Scientific Reports |
| title | Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs |
| title_full | Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs |
| title_fullStr | Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs |
| title_full_unstemmed | Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs |
| title_short | Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs |
| title_sort | auto detection of the coronavirus disease by using deep convolutional neural networks and x ray photographs |
| url | https://doi.org/10.1038/s41598-023-47038-3 |
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