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

• URL: http://arxiv.org/abs/2012.07079v4
• Date: Fri, 30 Apr 2021 11:03:07 GMT
• Title: CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images
• Authors: Narinder Singh Punn, Sonali Agarwal
• Abstract summary: The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide. 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. 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.
• Score: 0.6091702876917281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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