A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease
The chest Computer Tomography (CT scan) is used in the diagnosis of coronavirus disease 2019 (COVID-19) and is an important complement to the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The paper aims to improve the radiological diagnosis in the case of coronavirus disease COVID-1...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Radiology Research and Practice |
| Online Access: | http://dx.doi.org/10.1155/2021/5554408 |
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| author | Ahmed B. Salem Salamh Abdulrauf A. Salamah Halil Ibrahim Akyüz |
| author_facet | Ahmed B. Salem Salamh Abdulrauf A. Salamah Halil Ibrahim Akyüz |
| author_sort | Ahmed B. Salem Salamh |
| collection | DOAJ |
| description | The chest Computer Tomography (CT scan) is used in the diagnosis of coronavirus disease 2019 (COVID-19) and is an important complement to the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The paper aims to improve the radiological diagnosis in the case of coronavirus disease COVID-19 pneumonia on forms of noninvasive approaches with conventional and high-resolution computer tomography (HRCT) scan images upon chest CT images of patients confirmed with mild to severe findings. The preliminary study is to compare the radiological findings of COVID-19 pneumonia in conventional chest CT images with images processed by a new tool and reviewed by expert radiologists. The researchers used a new filter called Golden Key Tool (GK-Tool) which has confirmed the improvement in the quality and diagnostic efficacy of images acquired using our modified images. Further, Convolution Neural Networks (CNNs) architecture called VGG face was used to classify chest CT images. The classification has been performed by using VGG face on various datasets which are considered as a protocol to diagnose COVID-19, Non-COVID-19 (other lung diseases), and normal cases (no findings on chest CT). Accordingly, the performance evaluation of the GK-Tool was fairly good as shown in the first set of results, where 80–95% of participants show a good to excellent assessment of the new images view in the case of COVID-19 patients. The results, in general, illustrate good recognition rates in the diagnosis and, therefore, would be significantly higher in normal cases with COVID-19. These results could reduce the radiologist’s workload burden and play a major role in the decision-making process. |
| format | Article |
| id | doaj-art-9a3a46a4f0814abea11aae7e3c619c4b |
| institution | Kabale University |
| issn | 2090-1941 2090-195X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Radiology Research and Practice |
| spelling | doaj-art-9a3a46a4f0814abea11aae7e3c619c4b2025-02-03T05:52:57ZengWileyRadiology Research and Practice2090-19412090-195X2021-01-01202110.1155/2021/55544085554408A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus DiseaseAhmed B. Salem Salamh0Abdulrauf A. Salamah1Halil Ibrahim Akyüz2Institute of Science, Material Science and Engineering, Kastamonu University, Kuzey Kent /P.O. Box, 37150, Kastamonu, Kastamonu, TurkeyTripoli Central Hospital, P.O. Box 15528, Tripoli, LibyaComputer and Teaching Technologies Education, Kastamonu University, Kuzey Kent /P.O. Box, 37150, Kastamonu, Kastamonu, TurkeyThe chest Computer Tomography (CT scan) is used in the diagnosis of coronavirus disease 2019 (COVID-19) and is an important complement to the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The paper aims to improve the radiological diagnosis in the case of coronavirus disease COVID-19 pneumonia on forms of noninvasive approaches with conventional and high-resolution computer tomography (HRCT) scan images upon chest CT images of patients confirmed with mild to severe findings. The preliminary study is to compare the radiological findings of COVID-19 pneumonia in conventional chest CT images with images processed by a new tool and reviewed by expert radiologists. The researchers used a new filter called Golden Key Tool (GK-Tool) which has confirmed the improvement in the quality and diagnostic efficacy of images acquired using our modified images. Further, Convolution Neural Networks (CNNs) architecture called VGG face was used to classify chest CT images. The classification has been performed by using VGG face on various datasets which are considered as a protocol to diagnose COVID-19, Non-COVID-19 (other lung diseases), and normal cases (no findings on chest CT). Accordingly, the performance evaluation of the GK-Tool was fairly good as shown in the first set of results, where 80–95% of participants show a good to excellent assessment of the new images view in the case of COVID-19 patients. The results, in general, illustrate good recognition rates in the diagnosis and, therefore, would be significantly higher in normal cases with COVID-19. These results could reduce the radiologist’s workload burden and play a major role in the decision-making process.http://dx.doi.org/10.1155/2021/5554408 |
| spellingShingle | Ahmed B. Salem Salamh Abdulrauf A. Salamah Halil Ibrahim Akyüz A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease Radiology Research and Practice |
| title | A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease |
| title_full | A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease |
| title_fullStr | A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease |
| title_full_unstemmed | A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease |
| title_short | A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease |
| title_sort | study of a new technique of the ct scan view and disease classification protocol based on level challenges in cases of coronavirus disease |
| url | http://dx.doi.org/10.1155/2021/5554408 |
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