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

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-08, Vol.21 (17), p.5878
Main Authors: Bougourzi, Fares, Contino, Riccardo, Distante, Cosimo, Taleb-Ahmed, Abdelmalik
Format: Article
Language:English
Subjects:
Classification
Computed tomography
Computer Science
Computer vision
Coronaviruses
COVID-19
CT scans
Datasets
Deep Learning
Disease transmission
Emerging diseases
Human health and pathology
Humans
Infections
Infectious diseases
Life Sciences
Machine Learning
Medical Imaging
Medical research
multi-tasks strategy
Neural networks
Pandemics
Patients
Pneumonia
Pulmonology and respiratory tract
Recognition
SARS-CoV-2
Sensitivity
Severe acute respiratory syndrome coronavirus 2
slice-level classification
Tomography
Tomography, X-Ray Computed
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21175878