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

• Published in: IEEE access Vol. 9; pp. 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: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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; representation based classification; deep learning; machine learning; COVID-19 detection in early stages
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3064927

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.