Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans
Journal:
arXiv
Published Date:
May 18, 2025
Abstract
In this study, we propose a robust methodology for automatic segmentation of
infected lung regions in COVID-19 CT scans using convolutional neural networks.
The approach is based on a modified U-Net architecture enhanced with attention
mechanisms, data augmentation, and postprocessing techniques. It achieved a
Dice coefficient of 0.8658 and mean IoU of 0.8316, outperforming other methods.
The dataset was sourced from public repositories and augmented for diversity.
Results demonstrate superior segmentation performance. Future work includes
expanding the dataset, exploring 3D segmentation, and preparing the model for
clinical deployment.
