COVID-19 infection localization and severity grading from chest X-ray images
<p dir="ltr">The immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and mon...
محفوظ في:
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| مؤلفون آخرون: | , , , , , , , , , , , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513552561209344 |
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| author | Anas M. Tahir (16870077) |
| author2 | Muhammad E.H. Chowdhury (17151154) Amith Khandakar (14151981) Tawsifur Rahman (14150523) Yazan Qiblawey (16904838) Uzair Khurshid (17151157) Serkan Kiranyaz (3762058) Nabil Ibtehaz (16888773) M. Sohel Rahman (12056885) Somaya Al-Maadeed (5178131) Sakib Mahmud (15302404) Maymouna Ezeddin (16891368) Khaled Hameed (17151160) Tahir Hamid (16869921) |
| author2_role | author author author author author author author author author author author author author |
| author_facet | Anas M. Tahir (16870077) Muhammad E.H. Chowdhury (17151154) Amith Khandakar (14151981) Tawsifur Rahman (14150523) Yazan Qiblawey (16904838) Uzair Khurshid (17151157) Serkan Kiranyaz (3762058) Nabil Ibtehaz (16888773) M. Sohel Rahman (12056885) Somaya Al-Maadeed (5178131) Sakib Mahmud (15302404) Maymouna Ezeddin (16891368) Khaled Hameed (17151160) Tahir Hamid (16869921) |
| author_role | author |
| dc.creator.none.fl_str_mv | Anas M. Tahir (16870077) Muhammad E.H. Chowdhury (17151154) Amith Khandakar (14151981) Tawsifur Rahman (14150523) Yazan Qiblawey (16904838) Uzair Khurshid (17151157) Serkan Kiranyaz (3762058) Nabil Ibtehaz (16888773) M. Sohel Rahman (12056885) Somaya Al-Maadeed (5178131) Sakib Mahmud (15302404) Maymouna Ezeddin (16891368) Khaled Hameed (17151160) Tahir Hamid (16869921) |
| dc.date.none.fl_str_mv | 2021-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2021.105002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/COVID-19_infection_localization_and_severity_grading_from_chest_X-ray_images/24314347 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Information and computing sciences Artificial intelligence COVID-19 Chest X-ray Lung Segmentation Infection Segmentation Convolutional Neural Networks Deep Learning Reem Medical Center Qatar |
| dc.title.none.fl_str_mv | COVID-19 infection localization and severity grading from chest X-ray images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved outstanding performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation of Deep Learning models with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by an elegant human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2021.105002" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2021.105002</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_388281d96d22731432cd3b951b8b0ab1 |
| identifier_str_mv | 10.1016/j.compbiomed.2021.105002 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24314347 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | COVID-19 infection localization and severity grading from chest X-ray imagesAnas M. Tahir (16870077)Muhammad E.H. Chowdhury (17151154)Amith Khandakar (14151981)Tawsifur Rahman (14150523)Yazan Qiblawey (16904838)Uzair Khurshid (17151157)Serkan Kiranyaz (3762058)Nabil Ibtehaz (16888773)M. Sohel Rahman (12056885)Somaya Al-Maadeed (5178131)Sakib Mahmud (15302404)Maymouna Ezeddin (16891368)Khaled Hameed (17151160)Tahir Hamid (16869921)Biomedical and clinical sciencesCardiovascular medicine and haematologyClinical sciencesInformation and computing sciencesArtificial intelligenceCOVID-19Chest X-rayLung SegmentationInfection SegmentationConvolutional Neural NetworksDeep LearningReem Medical Center Qatar<p dir="ltr">The immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved outstanding performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation of Deep Learning models with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by an elegant human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2021.105002" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2021.105002</a></p>2021-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2021.105002https://figshare.com/articles/journal_contribution/COVID-19_infection_localization_and_severity_grading_from_chest_X-ray_images/24314347CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/243143472021-12-01T00:00:00Z |
| spellingShingle | COVID-19 infection localization and severity grading from chest X-ray images Anas M. Tahir (16870077) Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Information and computing sciences Artificial intelligence COVID-19 Chest X-ray Lung Segmentation Infection Segmentation Convolutional Neural Networks Deep Learning Reem Medical Center Qatar |
| status_str | publishedVersion |
| title | COVID-19 infection localization and severity grading from chest X-ray images |
| title_full | COVID-19 infection localization and severity grading from chest X-ray images |
| title_fullStr | COVID-19 infection localization and severity grading from chest X-ray images |
| title_full_unstemmed | COVID-19 infection localization and severity grading from chest X-ray images |
| title_short | COVID-19 infection localization and severity grading from chest X-ray images |
| title_sort | COVID-19 infection localization and severity grading from chest X-ray images |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Information and computing sciences Artificial intelligence COVID-19 Chest X-ray Lung Segmentation Infection Segmentation Convolutional Neural Networks Deep Learning Reem Medical Center Qatar |