Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans

Amal Lahchim; Lazar Davic

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arXiv · 2505.12298

Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans

Amal Lahchim·Lazar Davic

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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.

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Amal Lahchim, Lazar Davic. 2025-05-18. Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans. https://arxiv.org/abs/2505.12298

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