Classification of Corona Virus Infected Chest X-ray using Deep Convolutional Neural Network

Abstract

The coronavirus 2019 is a worldwide pandemic declared by the world health organization (WHO). It starts in China, Wuhan in November 2019 and spread all over the world. As time passed, the detection and clinical treatment of COVID-19 is developed by the researchers. COVID-19 is detected using a reverse transcription-polymerase chain reaction (RT-PCR) test, which is precise but requires two days to complete. Hence, the researchers proposed many classification models, which are mainly based on artificial intelligence. Mainly these classification models are using chest X-ray images for the detection of COVID-19. In this paper, we proposed a deep convolutional neural network model architecture to classify chest X-ray images. We called this model the base model, which is the first train to classify normal and abnormal chest X-ray images. Using the transfer learning technique, we retrained this model for four-classes classification (i.e., Normal, COVID-19, Pneumonia, and Pneumothorax). We obtain 73.9% accuracy for the base model (i.e., binary classification) and 83.2% accuracy for fine-tuned model (i.e., four-classes classification).