COVID-19 is currently one the most life-threatening problems around the
world. The fast and accurate detection of the COVID-19 infection is essential
to identify, take better decisions and ensure treatment for the patients which
will help save their lives. In this paper, we propose a fast and efficient way
to identify COVID-19 patients with multi-task deep learning (DL) methods. Both
X-ray and CT scan images are considered to evaluate the proposed technique. We
employ our Inception Residual Recurrent Convolutional Neural Network with
Transfer Learning (TL) approach for COVID-19 detection and our NABLA-N network
model for segmenting the regions infected by COVID-19. The detection model
shows around 84.67% testing accuracy from X-ray images and 98.78% accuracy in
CT-images. A novel quantitative analysis strategy is also proposed in this
paper to determine the percentage of infected regions in X-ray and CT images.
The qualitative and quantitative results demonstrate promising results for
COVID-19 detection and infected region localization.

该研究提出了一种基于多任务深度学习方法的快速有效的 COVID-19 患者识别方法，同时考虑 X 射线和 CT 扫描图像，使得 COVID-19 检测的测试准确率分别为 84.67％和 98.78％，并提出了一种定量分析策略以确定 X 射线和 CT 图像中的感染区域百分比，为 COVID-19 检测和感染区域定位提供了有前景的结果。