Journal
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
Volume 12, Issue 4, Pages 555-565Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12539-020-00393-5
Keywords
Two-step transfer learning; COVID19XrayNet; ResNet34; Feature smoothing layer (FSL); Feature extraction layer (FEL)
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Funding
- Jilin Provincial Key Laboratory of Big Data Intelligent Computing [20180622002JC]
- Education Department of Jilin Province [JJKH20180145KJ]
- Jilin University
- Bioknow MedAI Institute [BMCPP-2018-001]
- High Performance Computing Center of Jilin University
- Fundamental Research Funds for the Central Universities, JLU
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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. Graphic abstract COVID19XrayNet, a two-step transfer learning framework designed for biomedical images. [GRAPHICS] .
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