Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection
The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/7541447 |
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| _version_ | 1832545912322260992 |
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| author | Saud S. Alotaibi Ahmed Elaraby |
| author_facet | Saud S. Alotaibi Ahmed Elaraby |
| author_sort | Saud S. Alotaibi |
| collection | DOAJ |
| description | The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for medical diagnosis and a wide range of uses is computed tomography (CT). This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework. |
| format | Article |
| id | doaj-art-3acb26db914e41f698d1ba376eb97b60 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3acb26db914e41f698d1ba376eb97b602025-02-03T07:24:18ZengWileyComplexity1099-05262022-01-01202210.1155/2022/7541447Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 InfectionSaud S. Alotaibi0Ahmed Elaraby1Department of Information SystemsDepartment of Computer ScienceThe proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for medical diagnosis and a wide range of uses is computed tomography (CT). This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework.http://dx.doi.org/10.1155/2022/7541447 |
| spellingShingle | Saud S. Alotaibi Ahmed Elaraby Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection Complexity |
| title | Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection |
| title_full | Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection |
| title_fullStr | Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection |
| title_full_unstemmed | Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection |
| title_short | Generalized Exponential Fuzzy Entropy Approach for Automatic Segmentation of Chest CT with COVID-19 Infection |
| title_sort | generalized exponential fuzzy entropy approach for automatic segmentation of chest ct with covid 19 infection |
| url | http://dx.doi.org/10.1155/2022/7541447 |
| work_keys_str_mv | AT saudsalotaibi generalizedexponentialfuzzyentropyapproachforautomaticsegmentationofchestctwithcovid19infection AT ahmedelaraby generalizedexponentialfuzzyentropyapproachforautomaticsegmentationofchestctwithcovid19infection |