A deep learning approach for accurate COVID-19 diagnosis from x-ray images using OBLMPA - Xiaohua Li; Shuai Fu (AIP Advances)
Coronavirus is a virus from a large family that can infect humans and animals. Usually, the symptoms associated with a mild infection are similar to the common cold. COVID-19 is a new type of coronavirus that has not been seen in humans before and can infect anyone, because no one’s immune system is immune to coronavirus. People suspected of having COVID-19 should be informed immediately if they are actually infected with the virus so that they can isolate themselves, receive appropriate treatment, and inform those who have been in close contact with them. In this study, a new computer-aided method is proposed based on deep learning for the diagnosis of COVID-19 from the x-ray images. The suggested method proposes an optimal Convolutional Neural Network (CNN) to provide a diagnosis system with higher accuracy. The CNN has been optimized by an improved version of the Marine Predator Algorithm. The method is analyzed based on some different measurement indicators, and the results are compared with some state-of-the-art methods. Final simulations showed the higher efficiency of the proposed method toward the others.