Lung Segmentation in Chest X-ray Images using Edge Attention-based U-Net

Abstract

Accurate lung segmentation from chest X-rays is crucial for early disease diagnosis and monitoring of pulmonary diseases. For lung segmentation, CNN-based architecture plays a crucial role but still lacks some edge boundary detection issues while working with low-feature images. Previous research shows that there is still an issue in proper image segmentation with proper edge detection as in earlier methods like TVAC, Active Spline, Random Walker, U-Net as they focus on only high-level feature extraction from images and not low-level features, resulting in poor boundary detection. This research addresses this gap by proposing an Edge Attention-based U-Net architecture for proper edge boundary detection. This Edge Attention-based U-Net uses edge attention mechanisms to precisely locate lung boundaries while working with noisy and challenging X-ray imaging conditions. This improves segmentation accuracy and works efficiently in noisy situations, making it suitable for real-world clinical applications. Unlike previous methods the EA-U-Net surpasses them significantly. This superiority is evident through comprehensive comparisons conducted on the same Montgomery dataset of X-ray images during the training, validation, and testing phases. The EA-UNet consistently outperforms its predecessors, demonstrating higher accuracy metrics, including Dice mean, Jaccard Mean, and Pixel accuracy, while exhibiting superior boundary detection capabilities. The EA-U-Net's exceptional performance in lung segmentation enhances reliability and paves the way for advancements in computer-aided diagnosis and personalized healthcare. By providing clinicians with more accurate and reliable tools for analyzing chest X-rays, the EA-U-Net contributes significantly to improving patient care and medical decision-making processes.