Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
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) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, 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.
Cite
@article{arxiv.2005.03264,
title = {Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT},
author = {Liang Sun and Zhanhao Mo and Fuhua Yan and Liming Xia and Fei Shan and Zhongxiang Ding and Wei Shao and Feng Shi and Huan Yuan and Huiting Jiang and Dijia Wu and Ying Wei and Yaozong Gao and Wanchun Gao and He Sui and Daoqiang Zhang and Dinggang Shen},
journal= {arXiv preprint arXiv:2005.03264},
year = {2020}
}