Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images

Coronavirus 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...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.41052-41065
Main Authors: Ahishali, Mete, Degerli, Aysen, Yamac, Mehmet, Kiranyaz, Serkan, Chowdhury, Muhammad E. H., Hameed, Khalid, Hamid, Tahir, Mazhar, Rashid, Gabbouj, Moncef
Format: Article
Language:English
Subjects:
Chest
Classifiers
Computed tomography
Coronaviruses
COVID-19
COVID-19 detection in early stages
deep learning
Lung
Machine learning
Medical diagnostic imaging
Medical imaging
Physicians
Public health
representation based classification
Sensitivity
Task analysis
Viral diseases
Warning systems
X-ray imaging
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Summary:Coronavirus 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3064927