About: Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
Creator	Xia, Liming Yan, Fuhua Sun, L Jiang, H Shi, Feng Sun, Liang Wei, Ying Shan, Fei Shi, F Wu, D Zhang, D Gao, Y Fellow, Ieee Shao, Wei Ding, Zhongxiang Gao, Yaozong Gao, Wanchun Jiang, Huiting Mo, Z Mo, Zhanhao Shao, W Shen, D Shen, Dinggang Sui, H Sui, He Wei, Y Wu, Dijia Yan, F Yuan, H Yuan, Huan Zhang, Daoqiang
source	ArXiv
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.
has issue date	2020-05-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	3006935f138ba3430ed693ba4b9266f8f559dd27
resource representing a document's title	Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
resource representing a document's body	covid:3006935f138ba3430ed693ba4b9266f8f559dd27#body_text
is schema:about of	named entity 'community acquired pneumonia' named entity 'deep forest' named entity 'Deep Forest' named entity 'feature selection' named entity 'HIGH' named entity 'EFFECTIVE' named entity 'BASED' named entity 'SCALE' named entity 'COMMUNITY ACQUIRED PNEUMONIA' named entity 'DIAGNOSIS TECHNIQUE' named entity 'LEARN' named entity 'CAP' named entity 'ADAPTIVE' named entity 'trained' named entity 'leverage' named entity 'Experimental' named entity 'diagnosis' named entity 'clinicians' named entity 'SEN' named entity 'representation' named entity 'tool' named entity 'Chest' named entity 'capture' named entity 'Deep Forest' named entity 'Chest' named entity 'coronavirus disease' named entity 'COVID-19' named entity 'COVID-19' named entity 'feature selection' named entity 'COVID-19' named entity 'computed tomography' named entity 'deep forest' named entity 'Chest CT' named entity 'images' named entity 'COVID-19' named entity 'WHO' named entity 'infection' named entity 'chest CT' named entity 'cross-validation' named entity 'deep forest' named entity 'radiologists' named entity 'COVID-19' named entity 'feature selection' named entity 'feature selection' named entity 'CT scans' named entity 'training data' named entity 'COVID-19' named entity 'sparsity' named entity 'COVID-19' named entity 'voxels' named entity 'COVID-19' named entity 'deep forest' named entity 'COVID-19' named entity 'True Positive' named entity 'FAS' named entity 'Histogram' named entity 'COVID-19'

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