Covid-19 classification using CNN with radiography image data set

Abstract

COVID-19 is a novel coronavirus disease that has been reported in Wuhan, China since late December 2019 and has subsequently spread around the world. In severe cases of illness, there may be no option but to die due to substantial alveolar damage and progressive respiratory failure. Testing with RT-PCR, for instance, is the gold standard for clinical diagnosis, but it is possible for the tests to produce false negatives. Further, the lack of resources for conducting RT-PCR testing may deter the next clinical decision and treatment under the pandemic situation. As a result, chest CT imaging has become a valuable tool for diagnostic and prognostic purposes in COVID-19 patients. Detection of COVID-19 early enables the development of prevention plans and a disease control plan. Through this experimentation, the main objective is to utilize transfer learning to leverage pre-trained weights from CNNs. We propose the ResNet50 architecture based on the ImageNet pre-trained weights to detect the Covid-19. The proposed model is evaluated on X-ray images of COVID*19 chests and on images taken with a Computerized Tomography scanner. Using the 746 images of covid and non-covid patient datasets are bifurcated into train and test datasets for training and validate our model and achieved 84.90 % model accuracy. The Accuracy, precision, recall and F1-Scores are presented along with the receiver operating characteristic (ROC) curve, the precision-recall curve, the average prediction, and the confusion matrix of three distinct models.

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