Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/4471
Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification
Feng Shi.
Liming Xia.
Fei Shan.
Dijia Wu.
Ying Wei.
Huan Yuan.
Huiting Jiang.
Yaozong Gao.
He Sui.
Dinggang Shen.
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://arxiv.org/pdf/2003.09860v1.pdf
The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 patients of CAP underwent thin-section CT. All images were preprocessed to obtain the segmentations of both infections and lung fields, which were used to extract location-specific features. An infection Size Aware Random Forest method (iSARF) was proposed, in which subjects were automated categorized into groups with different ranges of infected lesion sizes, followed by random forests in each group for classification. Experimental results show that the proposed method yielded sensitivity of 0.907, specificity of 0.833, and accuracy of 0.879 under five-fold cross-validation. Large performance margins against comparison methods were achieved especially for the cases with infection size in the medium range, from 0.01% to 10%. The further inclusion of Radiomics features show slightly improvement. It is anticipated that our proposed framework could assist clinical decision making.
arxiv.org
2020
Artículo
https://arxiv.org/pdf/2003.09860v1.pdf
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

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