Title :

CNR-IEMN: a deep learning based approach to recognise covid-19 from CT-scan

Author(s) :

Bougourzi, Fares [Auteur]
Contino, Riccardo [Auteur]
Distante, Cosimo [Auteur]
Taleb-Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021

City :

Toronto

Country :

Canada

Start date of the conference :

2021-06-06

Journal title :

Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021

Publisher :

IEEE

Publication date :

2021-06

English keyword(s) :

COVID-19
Deep learning
Computer vision
Sensitivity
Pulmonary diseases
Conferences
Computer architecture
Multi-task strategy
Slice-Level classification
Covid-19
CT-scans

HAL domain(s) :

Sciences de l'ingénieur [physics]
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique

English abstract : [en]

The recognition of Covid-19 infection and distinguishing it from other Lung diseases from CT-scan is an emerging field in machine learning and computer vision community. In this paper, we proposed deep learning based ...
Show more >The recognition of Covid-19 infection and distinguishing it from other Lung diseases from CT-scan is an emerging field in machine learning and computer vision community. In this paper, we proposed deep learning based approach to recognize the Covid-19 infection from the CT-scans. Our approach consists of two main stages. In the first stage, we trained deep learning architectures with Multi-task strategy for Slice-Level classification. In the second stage, we used the previous trained models with XG-boost classifier to classify the whole CT-scan into Normal, Covid-19 or Cap class. The evaluation of our approach achieved promising results on the validation data of SPGC-COVID dataset. In more details, our approach achieved 87.75% as overall accuracy and 96.36%, 52.63% and 95.83% sensitivities for Covid-19, Cap and Normal, respectively. From other hand, our approach achieved the fifth place on the three test datasets of SPGC on COVID-19 challenge where our approach achieved the best result for Covid-19 sensitivity.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Comment :

ISBN 978-1-7281-7606-2 ; e-ISBN 978-1-7281-7605-5

Collections :

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

Source :

Harvested from HAL