Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an A daptive F eature S election guided D eep F orest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

中文翻译：

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自适应特征选择引导深度森林通过胸部 CT 进行 COVID-19 分类。

胸部计算机断层扫描 (CT) 成为辅助诊断冠状病毒病 19 (COVID-19) 的有效工具。由于COVID-19在全球范围内的爆发，使用基于CT图像的计算机辅助诊断技术对COVID-19进行分类可以很大程度上减轻临床医生的负担。在本文中，我们提出了一个A 适应性的F 特色S 选举指导D 埃普F orest (AFS-DF)，用于基于胸部 CT 图像的 COVID-19 分类。具体来说，我们首先从 CT 图像中提取特定位置的特征。然后，为了用相对小规模的数据捕获这些特征的高级表示，我们利用深度森林模型来学习特征的高级表示。此外，我们提出了一种基于训练的深度森林模型的特征选择方法，以减少特征的冗余，其中特征选择可以自适应地与COVID-19分类模型结合。我们在 COVID-19 数据集上评估了我们提出的 AFS-DF，其中包括 1495 名 COVID-19 患者和 1027 名社区获得性肺炎 (CAP) 患者。我们的方法实现的准确度 (ACC)、灵敏度 (SEN)、特异性 (SPE)、AUC、精密度和 F1 分数分别为 91.79%、93.05%、89.95%、96.35%、93.10% 和 93.07%。COVID-19 数据集上的实验结果表明，与 4 种广泛使用的机器学习方法相比，所提出的 AFS-DF 在 COVID-19 与 CAP 分类方面取得了优异的性能。