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COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.
Zhang, Ruochi; Guo, Zhehao; Sun, Yue; Lu, Qi; Xu, Zijian; Yao, Zhaomin; Duan, Meiyu; Liu, Shuai; Ren, Yanjiao; Huang, Lan; Zhou, Fengfeng.
  • Zhang R; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Guo Z; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
  • Sun Y; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
  • Lu Q; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
  • Xu Z; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
  • Yao Z; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Duan M; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Liu S; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Ren Y; College of Information Technology, Jilin Agricultural University, Changchun, 130118, Jilin, China.
  • Huang L; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Zhou F; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China. FengfengZhou@gmail.com.
Interdiscip Sci ; 12(4): 555-565, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-778130
ABSTRACT
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / X-Rays / Neural Networks, Computer / Coronavirus Infections / Clinical Laboratory Techniques / Deep Learning / Lung / Models, Biological Type of study: Diagnostic study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal: Interdiscip Sci Journal subject: Biology Year: 2020 Document Type: Article Affiliation country: S12539-020-00393-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / X-Rays / Neural Networks, Computer / Coronavirus Infections / Clinical Laboratory Techniques / Deep Learning / Lung / Models, Biological Type of study: Diagnostic study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal: Interdiscip Sci Journal subject: Biology Year: 2020 Document Type: Article Affiliation country: S12539-020-00393-5