2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | CHLG-1.3 | ||
| Paper Title | CNR-IEMN: a deep learning based approach to recognise Covid-19 from CT-scan | ||
| Authors | Fares Bougourzi, Riccardo Contino, Cosimo Distante, CNR, Italy; Abdelmalik Taleb-Ahmed, Univ. Polytechnique Hauts-de-France, Univ. Lille, France | ||
| Session | CHLG-1: COVID-19 Diagnosis | ||
| Location | Zoom | ||
| Session Time: | Monday, 07 June, 09:30 - 12:00 | ||
| Presentation Time: | Monday, 07 June, 09:30 - 12:00 | ||
| Presentation | Poster | ||
| Topic | Grand Challenge: COVID-19 Diagnosis | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | 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. | ||