Abstract
As per data available on WHO website, COVID-19 patients on 20 June 2020 have surpassed the figure of 8.7 million globally and around 4.6 lakhs have lost their life. The most common diagnostic test for COVID-19 detection is a Polymerase Chain Reaction (PCR) test. In highly populated developing countries like Brazil, India etc., there has been a severe shortage of PCR test-kits. Furthermore, the PCR-test is very specific and has lower sensitivity. In this research work, authors have designed a decision support system based on statistical features and edge maps of X-ray images to detect COVID-19 virus in a patient. Online available data sets of chest X-ray images have been used to train and test decision tree, K-nearest neighbour's, random forest, and multilayer perceptron machine learning classifiers. From the experimental results, it has found that the multilayer perceptron achieved 94% accuracy which is higher than the other classifiers.
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