Automatic COVID-19 diagnosis using deep learning features and support vector machines based on Bayesian optimization

Early diagnosis of COVID-19 and detection of infected people are crucial in taking preventative measures and treating the infected people. Artificial intelligence applications based on machine and deep learning techniques are more effective and applicable in such cases. In this work, an approach for automatic COVID-19 diagnosis using chest X-ray images is proposed. In this paper, AlexNet, VGG16, and VGG19 deep learning architectures were used to extract the useful and relevant features. These features were then used as inputs to the support vector machine (SVM) with two discrete outputs: COVID-19 or No-findings. Furthermore, the Bayesian optimization (BO) algorithm was used to tune the parameters of the SVM classifier and choose the optimal parameters. The results of the study indicate that the VGG16-SVM-BO and VGG19-SVM-BO models give the best performance with an accuracy of 99.47%. According to this result, the proposed approach can effectively contribute to the diagnosis of COVID-19.