Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Fares Bougourzi , Riccardo Contino , Cosimo Distante (1) , Abdelmalik Taleb-Ahmed (2, 3)

1 ISASI - Istituto di Scienze Applicate e Sistemi Intelligenti “Eduardo Caianiello”
2 IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
3 COMNUM - IEMN - COMmunications NUMériques - IEMN

Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

Mots clés: deep learning; multi-tasks strategy; slice-level classification; COVID-19; CT scans

Domaines: Sciences de l'ingénieur [physics]

Fares Bougourzi, Riccardo Contino, Cosimo Distante, Abdelmalik Taleb-Ahmed. Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN. Sensors, 2021, 21 (17), pp.5878. ⟨10.3390/s21175878⟩. ⟨hal-03429749⟩