| Abstract: | Deep learning based analyses of computed tomography (CT) images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19.Two ensemble strategies are considered:the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation;voting strategy.A database containing 8347 CT slices of COVID-19,common pneumonia and normal subjects was used as training and testing sets.Results show that the novel method can reach a high accuracy of 99.37％ (recall:0.9981;precision:0.9893),with an increase of about 7％in comparison to single-component models.And the average test accuracy is 95.62％ (recall:0.9587;precision:0.9559),with a corresponding increase of 5.2％.Compared with several latest deep learning models on the identical test set,our method made an accuracy improvement up to 10.88％.The proposed method may be a promising solution for the diagnosis of COVID-19. |
