In the global outbreak of corona-virus disease (COVID-19), it is of foremost priority to find an efficient and faster diagnosis method to reduce the transmission rate of the COVID-19 disease. Recently, deep learning-based detection methods have been proposed as a potential diagnostic tool, because there is much essential information in the radio-logical images for COVID-19 detection. This paper intends to develop a hybrid deep neural network (COVID19-ResCapsNet), using the X-ray images, to predict the risk at the onset of disease in patients suffering from COVID-19. The proposed COVID19-ResCapsNet method consists of two steps. The first step is mainly to extract features with the improved Residual feature extraction network. In the final step, several Capsule Networks are employed to classify COVID-19, and Non COVID-19. The proposed method is evaluated on two CXR image datasets(Dataset-1: 4236 images and Dataset-2: 2250 images) and the classification accuracy can reach 0.9988 on Dataset-1 and 0.9933 on Dataset-2 for binary classification cases. The results are compared with several state-of-the-art pre-trained classification models. The effects of two important hyper parameters: different optimizer and batch size, have also been investigated. Hence, the proposed COVID-19 detection model is effective and can be used as the supplementary tool in clinical application.
