CSIC 5011 / MATH 5473 Final Project
Imaging’s Potential to Assist in COVID-19 Crisis

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severeacute respiratory syndrome coronavirus 2 (SARS-CoV-2). An efficient and accu-rate abnormalities detection in chest X-rays (CXR) of infected patients can assisthealth care staff in the battle against COVID-19.

We investigate the potential of synthetic images additional to existing dataset to improveCOVID-19 CXR classification accuracy.

Members

• YS. CHEN
• WH. SUM
• HP. IP
• ZP. WU

Instruction

• csic_5011_project2_report_ChenSumIpWu.pdf is the final report.
• csic_5011_project2_slides_ChenSumIpWu.pdf is the presentation slides.
• presentation video link: https://youtu.be/i8ON-oZdBFk
• "Code" contains main source codes in the report.
• "Data" contains data collection information.
• "Results" contains some results.

Result Gallery

Real vs Synthetic Chest X-rays

Data Visualization

Classification Performance

Real Data - Overall Accuracy: 84.1%	Real + Synthetic Data - Overall Accuracy: 91%

Acknowledgements

Original data were collected from COVID-19 Radiography Database.

The code in "Classification.ipynb" borrows heavily from Kaggle code.

The code in "stylegan2-ada" borrows heavily from stylegan2-ada-pytorch.