COVID-19 Diagnosis in Chest X-ray Images by Combining Pre-trained CNN Models with Flat and Hierarchical Classification Approaches

Novel coronavirus disease 2019 (COVID-19) is highly contagious and can lead to serious medical complications. Early detection of COVID-19 is important to control the spread of the disease and reduce the associated mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is commonly used for COVID-19 diagnosis. However, the RT-PCR is time consuming, requires special materials, and might have limited detection sensitivity in mild cases. One of the promising complementary modalities to improve the detection and tracking of COVID-19 is X-ray imaging of the chest, but the task of interpreting chest X-ray images is challenging. Convolutional neural networks (CNNs) provide an effective computational tool for classifying chest X-ray images with the goal of achieving accurate COVID-19 diagnosis. This study investigates the application of two pre-trained CNN models, namely AlexNet and ResNet-50, using transfer learning to classify chest X-ray images as normal, pneumonia (non-COVID-19 pneumonia), and COVID-19. The transfer learning process was applied based on two classification approaches, which are the flat classification approach and the hierarchical classification approach. The performance of the proposed CNN-based classification schemes has been evaluated using a dataset that includes 8,703 chest X-ray images. The results indicate that the pre-trained CNN models combined with the hierarchical classification approach achieved effective classification of chest X-ray images. In particular, the pretrained AlexNet model that is combined with the hierarchical classification approach obtained macro-averaged classification specificity, sensitivity, and F1 score of 98:3%, 89:1%, and 91:4%, respectively. Furthermore, the pre-trained ResNet-50 model that is combined with the hierarchical classification approach achieved macro-averaged specificity, sensitivity, and F1 score of 97:4%, 95:2%, and 94:9%, respectively.