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

The new coronavirus &#x0028 COVID-19 &#x0029 , 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 &#x0028 CNNs &#x0029 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 &#x0028 MLP &#x0029 , and support vector machine &#x0028 SVM &#x0029 . 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 &#x0026 . For the other dataset, the best pair is DenseNet201 with MLP, achieving an accuracy and an F1-score of 95.6 &#x0026 . Thus, the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.