Automatic COVID-19 Detection from chest radiographic images using Convolutional Neural Network

Abstract

Abstract The global pandemic of the novel coronavirus that started in Wuhan, China has affected more than 50 million people worldwide and caused more than 1263,787 tragic deaths. To date, the COVID-19 virus is still spreading and affecting thousands of people. The main problem with testing for COVID-19 is that there are very few test kits available for a large number of affected or suspicious individuals. This leads to the need for automatic detection systems that use artificial intelligence. Deep learning is one of the most powerful AI tools available, so we recommend creating a convolutional neural network to detect COVID-19 positive patients from chest radiographs. According to previous studies, lung X-rays of COVID-19-positive patients show obvious characteristics, so this is a reliable method for testing patients, because X-ray examination of suspicious patients is easier than rt-PCR. Our model has been trained with 820 chest radiographic images (excluding data augmentation) collected from 3 databases, with a classification accuracy of 99.45% (training accuracy of 99.70%), sensitivity of 99.30% and specificity of 99.40 %, proved that our model has become a reliable COVID-19 detector.

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