Show simple item record

AuthorTahir A.M.
AuthorChowdhury M.E.H.
AuthorKhandakar A.
AuthorRahman T.
AuthorQiblawey Y.
AuthorKhurshid U.
AuthorKiranyaz, Mustafa Serkan
AuthorIbtehaz N.
AuthorRahman M.S.
AuthorAl-Maadeed S.
AuthorMahmud S.
AuthorEzeddin M.
AuthorHameed K.
AuthorHamid T.
Available date2022-04-26T12:31:17Z
Publication Date2021
Publication NameComputers in Biology and Medicine
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.compbiomed.2021.105002
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118547752&doi=10.1016%2fj.compbiomed.2021.105002&partnerID=40&md5=89bb837fa1e925b4b222bf19e37045f2
URIhttp://hdl.handle.net/10576/30585
AbstractThe 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%.
Languageen
PublisherElsevier Ltd
SubjectBiological organs
Convolutional neural networks
Deep learning
Diagnosis
Grading
Image segmentation
Iterative methods
Chest X-ray
Chest X-ray image
Convolutional neural network
Coronavirus disease 2019
Coronaviruses
Deep learning
Infection segmentation
Localisation
Lung segmentation
Performance
Coronavirus
Article
controlled study
coronavirus disease 2019
disease severity
human
image segmentation
predictive value
sensitivity and specificity
thorax radiography
diagnostic imaging
lung
thorax
X ray
COVID-19
Humans
Lung
SARS-CoV-2
Thorax
X-Rays
TitleCOVID-19 infection localization and severity grading from chest X-ray images
TypeArticle
Volume Number139
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record