Titre :

Deep-Covid-SEV: an Ensemble 2D and 3D CNN-Based Approach for Covid-19 Severity Prediction from 3D CT-SCANS

Auteur(s) :

Bougourzi, Fares [Auteur]
Laboratoire Images, Signaux et Systèmes Intelligents [LISSI]
Dornaika, Fadi [Auteur]
Universitat Autònoma de Barcelona = Autonomous University of Barcelona = Universidad Autónoma de Barcelona [UAB]
Nakib, Amir [Auteur]
Laboratoire Images, Signaux et Systèmes Intelligents [LISSI]
Distante, Cosimo [Auteur]
Istituto di Scienze Applicate e Sistemi Intelligenti “Eduardo Caianiello” [ISASI]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]

Titre de la manifestation scientifique :

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

Ville :

Rhodes Island

Pays :

Grèce

Date de début de la manifestation scientifique :

2023-06-04

Éditeur :

IEEE

Mot(s)-clé(s) en anglais :

Covid-19
Deep Leaning
CNN
Recognition
Severity

Discipline(s) HAL :

Physique [physics]
Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Since the advent of Covid-19 in late 2019, medical image analysis with artificial intelligence (AI) has become an important research topic. CT-scan imaging is an important diagnostic tool for this disease. This study is ...
Lire la suite >Since the advent of Covid-19 in late 2019, medical image analysis with artificial intelligence (AI) has become an important research topic. CT-scan imaging is an important diagnostic tool for this disease. This study is part of the 3rd COV19D competition for Covid-19 Severity Prediction, where we aim to close the significant gap between validation and testing results of the previous competition. To achieve this, we proposed two methods based on 2D and 3D CNN, respectively. Our 2D-CNN approach, called 2B-InceptResnet, includes two paths for segmented lungs and for infection of all slices of the input CT-scan, respectively. Each path contains a ConvLayer and an Inception-ResNet model pre-trained on ImageNet. In contrast, our 3D-CNN approach, known as Hybrid-DeCoVNet, consists of four blocks: Stem, four 3D-ResNet layers, classification head, and decision layer. Decision-based ensemble models are also created using these two proposed solutions with six training subsets.Our proposed approaches outperformed the baseline approach by 36% in the validation data of the 3rd COV19D competition for predicting the severity of Covid-19. In addition, our approach ranked second in the testing phase with an improvement of 14% compared to the baseline approach. These promising results demonstrate the potential of our novel method to improve the diagnosis and prognosis of Covid-19, which could contribute to the development of better treatment strategies and ultimately save lives.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL