CNR-IEMN: A Deep Learning Based Approach to Recognise Covid-19 from CT-Scan

• Main Authors: Bougourzi, Fares, Contino, Riccardo, Distante, Cosimo, Taleb-Ahmed, Abdelmalik
• Format: Conference Proceeding
• Language: English
• Subjects: Computer architecture; Computer vision; Conferences; COVID-19; CT-scans; Deep learning; Multi-task strategy; Pulmonary diseases; Sensitivity; Slice-Level classification
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9414185

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.