  Since 2019, the global COVID-19 outbreak has emerged as a crucial focus in
healthcare research. Although RT-PCR stands as the primary method for COVID-19
detection, its extended detection time poses a significant challenge.
Consequently, supplementing RT-PCR with the pathological study of COVID-19
through CT imaging has become imperative. The current segmentation approach
based on TVLoss enhances the connectivity of afflicted areas. Nevertheless, it
tends to misclassify normal pixels between certain adjacent diseased regions as
diseased pixels. The typical Binary cross entropy(BCE) based U-shaped network
only concentrates on the entire CT images without emphasizing on the affected
regions, which results in hazy borders and low contrast in the projected
output. In addition, the fraction of infected pixels in CT images is much less,
which makes it a challenge for segmentation models to make accurate
predictions. In this paper, we propose COVID-CT-H-UNet, a COVID-19 CT
segmentation network to solve these problems. To recognize the unaffected
pixels between neighbouring diseased regions, extra visual layer information is
captured by combining the attention module on the skip connections with the
proposed composite function Bi-category Hybrid Loss. The issue of hazy
boundaries and poor contrast brought on by the BCE Loss in conventional
techniques is resolved by utilizing the composite function Bi-category Hybrid
Loss that concentrates on the pixels in the diseased area. The experiment shows
when compared to the previous COVID-19 segmentation networks, the proposed
COVID-CT-H-UNet's segmentation impact has greatly improved, and it may be used
to identify and study clinical COVID-19.
