COVID-19 Detection Using Chest X-ray Images

COVID-19 is a respiratory infectious disease discovered in Wuhan, China, which later turned out to be a pandemic disease. The disease is spreading at a rate higher than what the world is prepared for, and hence, there is a huge shortage in testing and resources for it. To overcome this situation, the artificial intelligence community has been working hard to make use of some advanced technology to detect the presence of novel coronavirus. In our paper, we propose an ensemble 3-class classifier model with a stochastic hill-climbing optimisation algorithm for detecting infection in chest X-ray images. The novelty of our work involves the selection of optimal feature set from a feature set of handcrafted features and VGG-16 features using optimisation technique which is followed by a soft voting based ensemble classification. The proposed model achieved an overall F1-score of 0.997. Our dataset has Chest X-Ray images of all age groups and provides a more reliable and consistent result that can be used for the timely detection of COVID-19.