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The pandemic of novel SARS-CoV-2 also known as COVID-19 has been spreadingworldwide, causing rampant loss of lives. Medical imaging such as CT, X-ray,etc., plays a significant role in diagnosing the patients by presenting thevisual representation of the functioning of the organs. However, for anyradiologist analyzing such scans is a tedious and time-consuming task. Theemerging deep learning technologies have displayed its strength in analyzingsuch scans to aid in the faster diagnosis of the diseases and viruses such asCOVID-19. In the present article, an automated deep learning based model,COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functionsas a semantic hierarchical segmenter to identify the COVID-19 infected regionsfrom lungs contour via CT medical imaging using two cascaded residual attentioninception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inceptionU-Net model with spectral spatial and depth attention network (SSD) that isdeveloped with the contraction and expansion phases of depthwise separableconvolutions and hybrid pooling (max and spectral pooling) to efficientlyencode and decode the semantic and varying resolution information. The CHS-Netis trained with the segmentation loss function that is the defined as theaverage of binary cross entropy loss and dice loss to penalize false negativeand false positive predictions. The approach is compared with the recentlyproposed approaches and evaluated using the standard metrics like accuracy,precision, specificity, recall, dice coefficient and Jaccard similarity alongwith the visualized interpretation of the model prediction with GradCam++ anduncertainty maps. With extensive trials, it is observed that the proposedapproach outperformed the recently proposed approaches and effectively segmentsthe COVID-19 infected regions in the lungs.

CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images