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10.1109/JBHI.2020.3019505

http://scihub22266oqcxt.onion/10.1109/JBHI.2020.3019505
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32845849!8545164!32845849
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suck abstract from ncbi


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pmid32845849      IEEE+J+Biomed+Health+Inform 2020 ; 24 (10): 2798-2805
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  • Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT #MMPMID32845849
  • Sun L; Mo Z; Yan F; Xia L; Shan F; Ding Z; Song B; Gao W; Shao W; Shi F; Yuan H; Jiang H; Wu D; Wei Y; Gao Y; Sui H; Zhang D; Shen D
  • IEEE J Biomed Health Inform 2020[Oct]; 24 (10): 2798-2805 PMID32845849show ga
  • 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.
  • |*Betacoronavirus[MESH]
  • |COVID-19[MESH]
  • |COVID-19 Testing[MESH]
  • |Clinical Laboratory Techniques/*statistics & numerical data[MESH]
  • |Computational Biology[MESH]
  • |Coronavirus Infections/classification/*diagnosis/*diagnostic imaging[MESH]
  • |Databases, Factual/statistics & numerical data[MESH]
  • |Deep Learning[MESH]
  • |Humans[MESH]
  • |Neural Networks, Computer[MESH]
  • |Pandemics/classification[MESH]
  • |Pneumonia, Viral/classification/*diagnosis/*diagnostic imaging[MESH]
  • |Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data[MESH]
  • |Radiography, Thoracic/statistics & numerical data[MESH]
  • |SARS-CoV-2[MESH]


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