The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the excellent details about the structure of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging. The CHS-Net is developed with the two cascaded residual attention inception U-Net (RAIU-Net) models where first generates lungs contour maps and second generates COVID-19 infected regions. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD), consisting of contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the weighted average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed research works on the basis of standard metrics. With extensive trials, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs.
翻译:新型严重急性急性呼吸系统综合症冠状病毒2 (SARS-COV-2) (SARS-COV-2) 的流行性新型急性急性急性急性呼吸系统激素2 (SARS-COV-2) (又称COVID-19) 的流行性能一直在全球蔓延,造成大量生命损失。医学成像,如计算断层成像(CT)、X光等,通过展示器官结构的精细细节,在诊断病人方面起着重要作用。然而,对于分析这种扫描的任何放射师来说,这是一个乏味和耗时的任务。 新兴的深层学习技术在分析这种扫描以协助更快地诊断疾病和病毒(COVID-19-19-19)方面表现出了力量。在本篇文章中,一个基于自动深层学习的模型,COVID-19级分解网络(CHS-Net-Net-Net) 功能在通过CTV成像来鉴定肺部螺旋螺旋螺旋内受感染的区域的血压分级分级分级分级分级分级分级分级分级分级分级分级分级分级分级分级分级分析。