Deep learning-based detection of COVID-19 from chest x-ray images

Abstract

COVID-19 is a highly contagious infectious disease that has infected millions of people worldwide. Polymerase Chain Reaction (PCR) is the gold standard diagnostic test available for COVID-19 detection. Alternatively, medical imaging techniques, including chest X-ray (CXR), has been instrumental in diagnosis and prognosis of patients with COVID-19. Enabling the CXR with machine learning-based automated diagnosis will be important for rapid diagnosis of the disease by minimizing manual assessment of images by the radiologists. In this work, we developed a deep learning model that utilizes the transfer learning approach using a pre-trained Residual Network model. The Residual Network 50 (ResNet50) is trained from scratch by utilizing the initial architecture and pre-trained weights to provide the classification results. Two types of classification (two-class and three-class) is performed using the developed model. A cascaded approach is adopted for two-class classification where the classification is performed in two phases. The dataset used for training and evaluating the model comprises of 8,254 images in total out of which 1651 images were considered for testing the cascaded model (15 COVID-19) and three-class classification (51 COVID-19). The model was evaluated using accuracy, sensitivity, specificity, and F1-score metrics. Our cascaded model yielded an accuracy of 91.8% for classification of abnormal and normal cases and 97.9% for the classification of pneumonia and COVID-19 images. In the three-class classification, our model reported an accuracy of 92% in classifying normal, pneumonia (bacterial and viral) and COVID-19 cases.