Cervical cell nuclei segmentation based on GC-UNet

Cervical cell nuclei segmentation based on GC-UNet

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Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as canine spectra kc 3 intranasal single dose overlapping cells and blurred nuclei boundaries.This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation.At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge.A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained.

Further, a decoder grounded rawafricaonline.com in a global context attention block is employed to foster global feature interaction and refine the predicted masks.