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, Wei Shao, Feng Shi, Huan Yuan, Huiting Jiang, Dijia Wu, Ying Wei, Yaozong Gao, He Sui, Daoqiang Zhang, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

112 Citations (Scopus)


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 Adaptive Feature Selection guided Deep Forest (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.

Original languageEnglish
Article number9178424
Pages (from-to)2798-2805
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number10
Publication statusPublished - 2020 Oct


  • COVID-19 classification
  • chest CT
  • deep forest
  • feature selection

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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