Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT
- Authors
- Sun, Liang; Mo, Zhanhao; Yan, Fuhua; Xia, Liming; Shan, Fei; Ding, Zhongxiang; Song, Bin; Gao, Wanchun; Shao, Wei; Shi, Feng; Yuan, Huan; Jiang, Huiting; Wu, Dijia; Wei, Ying; Gao, Yaozong; Sui, He; Zhang, Daoqiang; Shen, Dinggang
- Issue Date
- 10월-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Feature extraction; Computed tomography; Lung; Forestry; Hospitals; Radiology; Diseases; COVID-19 classification; deep forest; feature selection; chest CT
- Citation
- IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.24, no.10, pp.2798 - 2805
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
- Volume
- 24
- Number
- 10
- Start Page
- 2798
- End Page
- 2805
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/52570
- DOI
- 10.1109/JBHI.2020.3019505
- ISSN
- 2168-2194
- Abstract
- 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.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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