Automatic COVID 19 Detection from chest radiographic images using Convolutional Neural Network
The global pandemic of the novel coronavirus that started in Wuhan, China has affected more than 2 million people worldwide and caused more than 130,000 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 99.61% (training accuracy of 99.59% the sensitivity of 99.21% and specificity of 99.29 proved that our model has become a reliable COVID 19 detector.
medRxiv