Computer-Aided System for COVID-19 Using Semi-supervised-based Ensemble Learning and Reinforcement Learning

Coronavirus Disease 2019(COVID-19) has shocked the world with its rapid spread and enormous threat to life and has continued up to the present. In this paper, a computer-aided system is proposed to detect infections and predict the disease progression of COVID-19. A high-quality CT scan database labeled with time-stamps and clinicopathologic variables is constructed to provide data support. To our knowledge, it is the only database with time relevance in the community. An object detection model is then trained to annotate infected regions. Using those regions, we detect the infections using a model with semi-supervised-based ensemble learning and predict the disease progression depending on reinforcement learning. We achieve an mAP of 0.92 for object detection. The accuracy for detecting infections is 98.46%, with a sensitivity of 97.68%, a specificity of 99.24%, and an AUC of 0.987. Significantly, the accuracy of predicting disease progression is 90.32% according to the timeline. It is a state-of-the-art result and can be used for clinical usage.