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.

过去几个月，随着需要更准确的自动化工具包，对 2019 年冠状病毒病 (COVID-19) 检测方法的研究有所增加。最近的研究表明，CT 扫描图像包含检测 COVID-19 疾病的有用信息。然而，缺乏大型且均衡的数据集限制了在实际诊断环境中使用检测方法的可能性，因为它们无法概括。事实上，当应用于在训练阶段使用的不同环境（例如，不同的设备或人群）中捕获的样本时，这些模型的性能很快就会变得不足。在本文中，提出了一种基于集成的新方法，可以使用 CT 扫描图像更准确地检测 COVID-19 疾病。这项工作使用预训练的深度网络（例如，VGG、Xception、和 ResNet）通过遗传算法进化，结合成一个集成架构，用于对肺叶的聚类图像进行分类。该研究在作为现有数据集成获得的新数据集上进行了验证。实验评估结果表明，集成分类器在保证有效性能的同时，也表现出了更好的泛化能力。