DRDA-Net: Deep Residual Dual-Attention Network with Multi-Scale Approach for Enhancing Liver and Tumor Segmentation from CT Images

DRDA-Net: Deep Residual Dual-Attention Network with Multi-Scale Approach for Enhancing Liver and Tumor Segmentation from CT Images

Liver cancer is a major global health challenge, significantly contributing to mortality rates. The accurate segmentation of liver and tumors from clinical CT images plays a crucial role in selecting therapeutic strategies for liver disease and treatment monitoring but remains challenging due to liv...

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Bibliographic Details
Main Authors: Wail M. Idress, Yuqian Zhao, Khalid A. Abouda, Shaodi Yang
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Applied Sciences
Subjects:
liver and tumor segmentation
deep learning
residual UNet
dual-attention mechanisms
CT Images
Online Access:https://www.mdpi.com/2076-3417/15/5/2311
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