  Lung segmentation in chest X-ray images is of paramount importance as it
plays a crucial role in the diagnosis and treatment of various lung diseases.
This paper presents a novel approach for lung segmentation in chest X-ray
images by integrating U-net with attention mechanisms. The proposed method
enhances the U-net architecture by incorporating a Convolutional Block
Attention Module (CBAM), which unifies three distinct attention mechanisms:
channel attention, spatial attention, and pixel attention. The channel
attention mechanism enables the model to concentrate on the most informative
features across various channels. The spatial attention mechanism enhances the
model's precision in localization by focusing on significant spatial locations.
Lastly, the pixel attention mechanism empowers the model to focus on individual
pixels, further refining the model's focus and thereby improving the accuracy
of segmentation. The adoption of the proposed CBAM in conjunction with the
U-net architecture marks a significant advancement in the field of medical
imaging, with potential implications for improving diagnostic precision and
patient outcomes. The efficacy of this method is validated against contemporary
state-of-the-art techniques, showcasing its superiority in segmentation
performance.
