  Since the pandemic of COVID-19, several deep learning methods were proposed
to analyze the chest Computed Tomography (CT) for diagnosis. In the current
situation, the disease course classification is significant for medical
personnel to decide the treatment. Most previous deep-learning-based methods
extract features observed from the lung window. However, it has been proved
that some appearances related to diagnosis can be observed better from the
mediastinal window rather than the lung window, e.g., the pulmonary
consolidation happens more in severe symptoms. In this paper, we propose a
novel Dual Window RCNN Network (DWRNet), which mainly learns the distinctive
features from the successive mediastinal window. Regarding the features
extracted from the lung window, we introduce the Lung Window Attention Block
(LWA Block) to pay additional attention to them for enhancing the
mediastinal-window features. Moreover, instead of picking up specific slices
from the whole CT slices, we use a Recurrent CNN and analyze successive slices
as videos. Experimental results show that the fused and representative features
improve the predictions of disease course by reaching the accuracy of 90.57%,
against the baseline with an accuracy of 84.86%. Ablation studies demonstrate
that combined dual window features are more efficient than lung-window features
alone, while paying attention to lung-window features can improve the model's
stability.
