Journal
PROCEEDINGS OF 2020 IEEE 10TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2020)
Volume -, Issue -, Pages 281-284Publisher
IEEE
DOI: 10.1109/iceiec49280.2020.9152329
Keywords
deep learning; covid-19 detection; covid-net; transfer learning; model integration
Categories
Funding
- National Natural Science Foundation of China [61871039, 61802019, 61906017]
- Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing [IDHT20170511]
- Premium Funding Project for Academic Human Resources Development in Beijing Union University [BPHR2019AZ01]
- Beijing municipal commission of education project [KM201911417001]
- Big data collaborative innovation center for intelligent driving [CYXC1902]
- Beijing Union University project [202011417004, 202011417005, 202011417SJ025]
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We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest X-Ray images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.
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