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AuthorYamac M.
AuthorAhishali M.
AuthorDegerli A.
AuthorKiranyaz, Mustafa Serkan
AuthorChowdhury M.E.H.
AuthorGabbouj M.
Available date2022-04-26T12:31:18Z
Publication Date2021
Publication NameIEEE Transactions on Neural Networks and Learning Systems
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TNNLS.2021.3070467
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85104662870&doi=10.1109%2fTNNLS.2021.3070467&partnerID=40&md5=8a08ae41ba5a211b25d2779ba57a05f8
URIhttp://hdl.handle.net/10576/30589
AbstractCoronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectConvolution
Deep learning
Diagnosis
Classification scheme
Classification tasks
Diagnosis and prognosis
Diagnosis performance
Learning techniques
Neural network (nn)
Sparse representation
State-of-the-art performance
Classification (of information)
bacterial pneumonia
classification
diagnostic imaging
differential diagnosis
human
virus pneumonia
X ray
x-ray computed tomography
COVID-19
Deep Learning
Diagnosis, Differential
Humans
Neural Networks, Computer
Pneumonia, Bacterial
Pneumonia, Viral
Tomography, X-Ray Computed
X-Rays
TitleConvolutional Sparse Support Estimator-Based COVID-19 Recognition from X-Ray Images
TypeArticle
Pagination1810-1820
Issue Number5
Volume Number32
dc.accessType Open Access


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