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An Efficient DA-Net Architecture for Lung Nodule Segmentation
2021
Mathematics
A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule
doi:10.3390/math9131457
fatcat:3ohogfm74zdjhpqptzm5i3wmra