Abstract
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.
Highlights
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has declared as pandemic by the World Health Organization (WHO) on the 11th of March, 2020
The same denoiser matrix is used in the Collaborative Representation based Classification (CRC) method which is reported separately as the CRC-light version to observe if the Convolutional Support Estimator Network (CSEN) approach brings performance improvement
Considering the second group of approaches based on deep Convolutional Neural Networks (CNNs), DenseNet-121 is initialized with ChestX-ray14 weights, and it is fine-tuned over Initial Early-QaTa-COV19
Summary
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has declared as pandemic by the World Health Organization (WHO) on the 11th of March, 2020. RT-PCR is known to have a low sensitivity, and it is reported in [3] that RT-PCR has around 30 − 60% total positive rate for throat samples, and low positive rates occur especially in mild cases. To this end, there are studies [4]–[6] that investigate the usage of Chest-CTs and correlation between Chest-CT and RT-PCR tests as diagnostic tools. The ∞-norm and 0-norm are defined for the vector x as x n = maxi=1,...,n (|xi|) and limp→0 n i=1 = ∞ #{j : xj.
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