  Lung segmentation in chest X-ray images is a critical task in medical image
analysis, enabling accurate diagnosis and treatment of various lung diseases.
In this paper, we propose a novel approach for lung segmentation by integrating
Hierarchical SegNet with a proposed multi-modal attention mechanism. The
channel attention mechanism highlights specific feature maps or channels
crucial for lung region segmentation, while the context attention mechanism
adaptively weighs the importance of different spatial regions. By combining
both mechanisms, the proposed mechanism enables the model to better capture
complex patterns and relationships between various features, leading to
improved segmentation accuracy and better feature representation. Furthermore,
an attention gating mechanism is employed to integrate attention information
with encoder features, allowing the model to adaptively weigh the importance of
different attention features and ignore irrelevant ones. Experimental results
demonstrate that our proposed approach achieves state-of-the-art performance in
lung segmentation tasks, outperforming existing methods. The proposed approach
has the potential to improve the accuracy and efficiency of lung disease
diagnosis and treatment, and can be extended to other medical image analysis
tasks.
