Reinforcement learning based diagnosis and prediction for COVID-19 from CT images

ABSTRACT

As we all know, COVID-19 is causing more and more human infections and deaths. In order to quickly and efficiently detect COVID-19, this paper has firstly proposed a detection framework based on reinforcement learning for COVID-19 diagnosis. We use the accuracy of the validation set as the reward value, and obtain the initial model for the next epoch by searching the model corresponding to the maximum reward value in each epoch. We also have proposed a prediction framework that integrates multiple detection frameworks using parameter sharing to predict the progression of patients' disease. We experimented with our own dataset screened by professional physicians and obtained more excellent results. In external validation, we still achieved a high accuracy rate without additional training. Finally, the experimental results show that our classification accuracy can reach 96.81%, and the precision, sensitivity, specificity, and AUC (Area Under Curve) are 95.47%, 98.64%, 95.91%, and 0.9698, respectively. The accuracy of external verification can reach 93.04% and 90.85%. The accuracy of our prediction framework is 91.04%. A large number of experiments have proved that our proposed method is effective and robust for COVID-19 detection and prediction. © SPIE 2021.