🤖 AI Summary
To address insufficient segmentation accuracy of infected lung regions in COVID-19 CT images, this paper proposes an enhanced U-Net architecture incorporating a Channel-and-Spatial Coordinated Attention Mechanism (CBAM). The CBAM module is innovatively embedded into both encoder and decoder pathways to jointly strengthen perception of subtle lesion microstructures and suppress background interference. Additionally, multi-scale data augmentation and morphological post-processing are integrated to improve model robustness. Evaluated on a public benchmark dataset, the proposed method achieves a Dice coefficient of 0.8658 and a mean IoU of 0.8316—significantly outperforming standard U-Net and state-of-the-art baseline models. This work delivers a high-precision, interpretable segmentation tool for clinical decision support in COVID-19 diagnosis.
📝 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.