ILC-Unet++ for Covid-19 Infection Segmentation - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

ILC-Unet++ for Covid-19 Infection Segmentation

Résumé

Since the appearance of Covid-19 pandemic, in the end of 2019, Medical Imaging has been widely used to analysis this disease. In fact, CT-scans of the Lung can help to diagnosis, detect and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. In more details, we propose a CNN-based segmentation architecture named ILC-Unet++. The proposed ILC-Unet++ architecture, which is trained for both Covid-19 Infection and Lung Segmentation. The proposed architecture were tested using three datasets with two scenarios (intra and cross datasets). The experimental results showed that the proposed architecture performs better than three baseline segmentation architectures (Unet, Unet++ and Attention-Unet) and two Covid-19 infection segmentation architectures (SCOATNet and nCoVSegNet).
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Dates et versions

hal-03748005 , version 1 (09-08-2022)

Identifiants

Citer

Fares Bougourzi, Cosimo Distante, Fadi Dornaika, Abdelmalik Taleb-Ahmed, Abdenour Hadid. ILC-Unet++ for Covid-19 Infection Segmentation. International Conference on Image Analysis and Processing, ICIAP 2022 Workshops - Image Analysis and Processing, May 2022, Lecce, Italy. pp.461-472, ⟨10.1007/978-3-031-13324-4_39⟩. ⟨hal-03748005⟩
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