The model consists of: UNet Encoder: Captures spatial features effectively. BiFPN Decoder: Enhances multi-scale feature fusion for better segmentation results. Skip Connections: Allow better gradient ...

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.

The encoder-decoder structure are widely used in almost every semantic and instance segmentation task. Their success is largely attributed to the design of the skip connections that combine the deep, ...

Figure 1.Illustrations of different types of encoders, the structures of encoders (A–C) are derived from UNet's encoder, decoder, and full structure, respectively. C1 represents a feature map of the ...

After a traumatic brain injury (TBI), there is a risk of intracranial hemorrhage (ICH) occurring, which can have severe consequences such as death or disability. Prompt and accurate diagnosis, ...

To solve the aforementioned problems, in this study, we developed a feature fusion network based on the multi-encoder and single-decoder structure, named MM-UNet, which extracts the corresponding ...

UNet is a specialized type of convolutional network, particularly well-suited for semantic segmentation tasks. The architecture consists of a contracting path (encoder) ...