CNR-IEMN: a deep learning based approach ...
Document type :
Communication dans un congrès avec actes
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]
Tahleb Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Contino, Riccardo [Auteur]
Distante, Cosimo [Auteur]
Tahleb 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
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
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 >
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
Source :
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