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AuthorAhishali M.
AuthorDegerli A.
AuthorYamac M.
AuthorKiranyaz, Mustafa Serkan
AuthorChowdhury M.E.H.
AuthorHameed K.
AuthorHamid T.
AuthorMazhar R.
AuthorGabbouj M.
Available date2022-04-26T12:31:19Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3064927
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102649074&doi=10.1109%2fACCESS.2021.3064927&partnerID=40&md5=f0e8268f87079da7c71027c1d91e22e7
URIhttp://hdl.handle.net/10576/30600
AbstractCoronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectConvolutional neural networks
Deep learning
Diagnosis
HTTP
Medical imaging
Benchmark datasets
Chest X-ray image
Data classification
Early diagnosis
Learning approach
Machine learning techniques
Medical doctors
Software implementation
Learning systems
TitleAdvance Warning Methodologies for COVID-19 Using Chest X-Ray Images
TypeArticle
Pagination41052-41065
Volume Number9
dc.accessType Abstract Only


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