A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images
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In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques to early detect COVID-19 from plain 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 175 early-stage COVID-19 Pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 1579 samples for control (normal) class. A detailed set of experiments show that the CSEN achieves the top (over 98.5 learning over the deep CheXNet fine-tuned with the augmented data produces the leading performance among other deep networks with 97.14 99.49
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