Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans
This addresses the need for accurate medical image analysis in COVID-19 diagnosis, but it is incremental as it builds on existing U-Net methods.
The study tackled the problem of automatically segmenting COVID-19 infected lung regions in CT scans by proposing a modified U-Net architecture with attention mechanisms, achieving a Dice coefficient of 0.8658 and mean IoU of 0.8316.
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.