The dataset used for training and evaluation is sourced from this paper. It contains a diverse set of images and corresponding segmentation masks.

UNet and other segmentation models based on the encoder-decoder architecture tend to fuse semantically dissimilar feature maps from the encoder and decoder sub-networks, which may degrade segmentation ...

Prompt and accurate diagnosis, followed by timely treatment, is crucial to prevent further complications.This study proposes a method for segmenting computed tomography (CT) images based on an encoder ...

In this paper, we propose a deep architecture for semantic segmentation from scratch based on an asymmetry encoder- decoder architecture using Ghost-Net and U-Net which we have called it Ghost-UNet.

In this article, the effects of different parts of the U-Net on the segmentation ability are experimentally analyzed. Then a more efficient architecture, Half-UNet, is proposed. The proposed ...

Therefore, in this study, we developed a multimodality feature fusion network, MM-UNet, for brain tumor segmentation by adopting a multi-encoder and single-decoder structure. In the proposed network, ...

UNet is a specialized type of convolutional network, particularly well-suited for semantic segmentation ... path (encoder) to capture context and a symmetric expanding path (decoder) that enables ...