期刊论文详细信息
PATTERN RECOGNITION 卷:120
Deep neural networks ensemble to detect COVID-19 from CT scans
Article
Aversano, Lerina1  Bernardi, Mario Luca1  Cimitile, Marta2  Pecori, Riccardo1 
[1] Univ Sannio, Benevento, BN, Italy
[2] Unitelma Sapienza Univ, Rome, Italy
关键词: Deep learning;    CT Scan images;    COVID-19;    Coronavirus;   
DOI  :  10.1016/j.patcog.2021.108135
来源: Elsevier
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【 摘 要 】

Research on Coronavirus Disease 2019 (COVID-19) detection methods has increased in the last months as more accurate automated toolkits are required. Recent studies show that CT scan images contain useful information to detect the COVID-19 disease. However, the scarcity of large and well balanced datasets limits the possibility of using detection approaches in real diagnostic contexts as they are unable to generalize. Indeed, the performance of these models quickly becomes inadequate when applied to samples captured in different contexts (e.g., different equipment or populations) from those used in the training phase. In this paper, a novel ensemble-based approach for more accurate COVID-19 disease detection using CT scan images is proposed. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet) evolved with a genetic algorithm, combined into an ensemble architecture for the classification of clustered images of lung lobes. The study is validated on a new dataset obtained as an integration of existing ones. The results of the experimental evaluation show that the ensemble classifier ensures effective performance, also exhibiting better generalization capabilities. (c) 2021 Elsevier Ltd. All rights reserved.

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