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[2505.12298] Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans
Summary
This article presents a novel approach using an Attention-Enhanced U-Net for the automatic segmentation of COVID-19 infected lung regions in CT scans, achieving high accuracy metrics.
Why It Matters
Accurate segmentation of lung regions affected by COVID-19 is crucial for diagnosis and treatment planning. This study contributes to the field of medical imaging by enhancing existing methodologies, potentially improving clinical outcomes.
Key Takeaways
- Introduces an Attention-Enhanced U-Net architecture for improved segmentation.
- Achieved a Dice coefficient of 0.8658 and mean IoU of 0.8316, outperforming existing methods.
- Utilizes data augmentation to enhance dataset diversity.
- Future work includes expanding datasets and exploring 3D segmentation.
- Highlights the importance of preparing models for clinical deployment.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2505.12298 (eess) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 18 May 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans Authors:Amal Lahchim (University of Kragujevac), Lazar Davic (University of Kragujevac) View a PDF of the paper titled Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans, by Amal Lahchim (University of Kragujevac) and 1 other authors View PDF 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...