The pandemic of novel SARS-CoV-2 also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as CT, X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning 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 using two cascaded residual attention inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD) that is developed with the 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 defined as the 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 approaches and evaluated using the standard metrics like accuracy, precision, specificity, recall, dice coefficient and Jaccard similarity along with the visualized interpretation of the model prediction with GradCam++ and uncertainty maps. 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SARS-COV-2(又称COVID-19)的流行性新SARS-COV-2(又称COVID-19)的流行已在全世界蔓延,造成大量生命损失。在本篇文章中,以自动深层次学习为基础的模型(COVID-19级分解网络(CHS-Net))在通过展示器官功能的视觉表现来诊断病人方面起着重要作用。然而,对于任何分析这种扫描的放射学家来说,这是一个乏味和耗时的任务。新兴的深层次学习技术在分析这种扫描以协助更快地诊断疾病和病毒(如COVID-19-19)方面表现出了力量,从而导致大量丧失生命。 以自动深层次学习为基础的模型(COVID-19级分解网络网络网络)为主,作为静态分层分层分解分析器,通过CT医学成像来识别肺部内受COVID-19感染的区域。 新的内存留关注状态(RAU-Net)模式(RAU-Net)模式由残余状态模型模型模型和深层注意方法(SSD)组成,该模型与测得的精度分析阶段的精度和内存的直径变变变变变变变变的变的变的内和变的货币和变的变的变的货币和变的计算, 数据和混合的变的变的计算和变的计算,其变现的变的变的计算和变的变的计算和变的计算和变的变的计算,其变的计算和变的精功能功能是被定义的计算,其变的计算, 和变的变的变的精度和变的变的计算和变的变的变的变的计算,其变的计算,其变的变的计算和变的变的计算和变的变的计算, 和变的变的变的计算, 的计算和变的计算, 的计算和变的计算和变的精的计算, 和变的变的变的计算的变的变的变的变的变的变的计算是的计算的变的代的变的变的变的计算和光的变的计算的计算的变的变的代的代的代的代的代的代的代的代的代的代的代的代的代的计算