Title :

ILC-Unet++ for Covid-19 Infection Segmentation

Author(s) :

Bougourzi, Fares [Auteur correspondant]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Distante, Cosimo [Auteur]
Istituto di Scienze Applicate e Sistemi Intelligenti “Eduardo Caianiello” [ISASI]
Dornaika, Fadi [Auteur]
University of the Basque Country/Euskal Herriko Unibertsitatea [UPV/EHU]
Ikerbasque - Basque Foundation for Science
Universitat Autònoma de Barcelona [UAB]
Taleb-Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Hadid, Abdenour [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

International Conference on Image Analysis and Processing, ICIAP 2022 Workshops - Image Analysis and Processing

City :

Lecce

Country :

Italie

Start date of the conference :

2022-05-23

Journal title :

Lecture Notes in Computer Science

Publisher :

Springer International Publishing

Publication date :

2022-08-04

English keyword(s) :

Covid-19
Segmentation
Deep learning

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

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 ...
Show more >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).Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL