mlsanityData to 14 Oct 2026

A Novel Approach to Chest X-ray Lung Segmentation Using U-net and Modified Convolutional Block Attention Module

arXiv 2404.14322 ↗First posted 22 Apr 2024eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 22 Apr 2024.

Builds on

11 of 34 references.

References · 11

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  3. 2020Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualizationcited by 50
  4. 2020Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentationcited by 17
  5. 2023Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networkscited by 10
  6. 2024Concrete Surface Crack Detection with Convolutional-based Deep Learning Modelscited by 4
  7. 2024Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicatorscited by 4
  8. 2024The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Gamecited by 4
  9. 2024Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Processcited by 3
  10. 2020Evolution of diversity and dominance of companies in online activitycited by 1
  11. 2023Trading-off Mutual Information on Feature Aggregation for Face Recognitioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approachcited by 6
  2. 2024Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Imagescited by 2
  3. 2026From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environmentscited by 0