Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

The new coronavirus ( COVID-19 ) , declared by the World Health Organization as a pandemic, has infected more than 1 million people and killed more than 50 thousand An infection caused by COVID-19 can develop into pneumonia, which can be detected by a chest X-ray exam and should be treated appropriately In this work, we propose an automatic detection method for COVID-19 infection based on chest X-ray images The datasets constructed for this study are composed of 194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients Since few images of patients with COVID-19 are publicly available, we apply the concept of transfer learning for this task We use different architectures of convolutional neural networks ( CNNs ) trained on ImageNet, and adapt them to behave as feature extractors for the X-ray images Then, the CNNs are combined with consolidated machine learning methods, such as k-Nearest Neighbor, Bayes, Random Forest, multilayer perceptron ( MLP ) , and support vector machine ( SVM ) The results show that, for one of the datasets, the extractor-classifier pair with the best performance is the MobileNet architecture with the SVM classifier using a linear kernel, which achieves an accuracy and an F1-Score of 98 5% For the other dataset, the best pair is DenseNet201 with MLP, achieving an accuracy and an F1-Score of 95 6% Thus, the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images IEEE