论文标题
COVID_MTNET：COVID-19用多任务深度学习方法检测
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
论文作者
论文摘要
Covid-19目前是世界上最危及生命最大的问题之一。快速准确检测COVID-19感染对于识别，做出更好的决定并确保对患者的治疗至关重要，这将有助于挽救他们的生命。在本文中，我们提出了一种快速有效的方法，以识别具有多任务深度学习（DL）方法的Covid-19患者。 X射线和CT扫描图像均被认为用于评估所提出的技术。我们使用转移学习方法（TL）方法进行共同检测和NABLA-N网络模型，以分割由COVID-19感染的区域进行分割。检测模型显示了X射线图像的测试准确性约为84.67％，CT图像的精度为98.78％。本文还提出了一种新型的定量分析策略，以确定X射线和CT图像中受感染区域的百分比。定性和定量结果证明了COVID-19检测和感染区域定位的有希望的结果。
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.
