The year 2020 will certainly be remembered for the outbreak of COVID-19 pandemic. With the first case being reported in December 2019, the SARSCoV2 virus has proved to be one of the most deadly virus which has affected the human civilization. Relatively high contagious rate and asymptomatic patients also being carrier of the virus makes it more dangerous. The only way to get a control on the outbreak is rapid testing. With the present testing mechanisms being costly and time consuming the end of this pandemic doesn't seems near. These challenges motivates us to come up with a system which can be effective in testing large population size and at the same time be less time consuming. We have proposed a Deep Convotuional Neural Network based ensemble architecture for extracting features from Chest X-Ray images and later classifying them into three categories namely-Community Acquired Pneumonia(CAP), Normal and COVID-19. We have shown that applying such technique can give better performance. Our ensemble network uses three pre-trained DCNN networks namely-NASNet, MobileNet and DenseNet. The low level features extracted from the three DCNN architectures are later concatenated and applied to a classifier for final classification. We have achieved an accuracy of 91.99% which is slightly better than the state of the art performances.
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