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Fuhua Yan

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JBHI Journal 2020 Journal Article

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT

  • Liang Sun
  • Zhanhao Mo
  • Fuhua Yan
  • Liming Xia
  • Fei Shan
  • Zhongxiang Ding
  • Bin Song
  • Wanchun Gao

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.

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