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 tha...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.展开更多
基金the Sichuan Science and Technology Department Research and Development Key Project(No.21ZDYF3607)the Weining Cloud Hospital Based AI Medical Software System Service and Demo Project(No.2019K0JTS0159)the China Postdoctoral Science Foundation(No.2020T130137ZX)。
文摘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.
