An early effective screening and grading of COVID-19 has become imperative towards optimizing the limited available resources of the medical facilities. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Composite Deep network with Feature Weighting (CDNetFW), is proposed for efficient delineation of infected regions from lung CT images. Initially a coarser-segmentation is performed directly at shallower levels, thereby facilitating discovery of robust and discriminatory characteristics in the hidden layers. The novel feature weighting module helps prioritise relevant feature maps to be probed, along with those regions containing crucial information within these maps. This is followed by estimating the severity of the disease.The deep network CDNetFW has been shown to outperform several state-of-the-art architectures in the COVID-19 lesion segmentation task, as measured by experimental results on CT slices from publicly available datasets, especially when it comes to defining structures involving complex geometries.

翻译：对COVID-19的早期有效筛选和分级对于优化医疗设施的有限可用资源至关重要,对肺部CT感染量的自动分解将大大有助于诊断和护理病人,然而,由于肺部结构和位置不固定,对损伤的准确划分仍成问题。为了从肺部CT图像中有效划分受感染地区,建议建立一个全新的深层学习结构,即综合深层网络和地貌重力(CDNetFW),最初在浅层直接进行粗略分解,从而便利发现隐蔽层的坚固和歧视性特征。新特征加权模块有助于优先调查相关特征图,以及含有这些地图中关键信息的区域。随后,对疾病的严重程度进行了估计。根据公开数据集中CT切片的实验结果测量,深海网络CDNetFW超越了COVID-19分解任务中的若干州级结构。