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

arXiv: https://arxiv.org/abs/2404.14322 · HTML: https://mlsanity.com/paper/2404.14322/

## 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 (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation](https://mlsanity.com/paper/2003.10304/) (2020)
- [Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks](https://mlsanity.com/paper/2304.04199/) (2023)
- [Concrete Surface Crack Detection with Convolutional-based Deep Learning Models](https://mlsanity.com/paper/2401.07124/) (2024)
- [Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators](https://mlsanity.com/paper/2403.03606/) (2024)
- [The Correlations of Scene Complexity, Workload, Presence, and Cybersickness in a Task-Based VR Game](https://mlsanity.com/paper/2403.19019/) (2024)
- [Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process](https://mlsanity.com/paper/2403.10842/) (2024)
- [Evolution of diversity and dominance of companies in online activity](https://mlsanity.com/paper/2003.07049/) (2020)
- [Trading-off Mutual Information on Feature Aggregation for Face Recognition](https://mlsanity.com/paper/2309.13137/) (2023)

## Built on by (most cited first, 3 of 3)

- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [Hierarchical SegNet with Channel and Context Attention for Accurate Lung Segmentation in Chest X-ray Images](https://mlsanity.com/paper/2405.12318/) (2024)
- [From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments](https://mlsanity.com/paper/2603.23964/) (2026)

## 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.