CNR-IEMN: a deep learning based approach to recognise covid-19 from CT-scan - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

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

Résumé

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.

Dates et versions

hal-03261646 , version 1 (15-06-2021)

Identifiants

Citer

Fares Bougourzi, Riccardo Contino, Cosimo Distante, Abdelmalik Taleb-Ahmed. CNR-IEMN: a deep learning based approach to recognise covid-19 from CT-scan. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Jun 2021, Toronto, Canada. pp.8568-8572, ⟨10.1109/ICASSP39728.2021.9414185⟩. ⟨hal-03261646⟩
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