ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception
Gongtao Yue,
Chen Yang,
Zhengyang Zhao
et al.
Abstract:The Segmentation of infected areas from COVID-19 chest X-ray (CXR) images is of great significance for the diagnosis and treatment of patients. However, accurately and effectively segmenting infected areas of CXR images is still challenging due to the inherent ambiguity of CXR images and the cross-scale variations in infected regions. To address these issues, this article proposes a ERGPNet based on embedded residuals and global perception, to segment lesion regions in COVID-19 CXR images. First, aiming at the… Show more
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