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?:abstract
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In late 2019, the first case of COVID-19 was confirmed in Wuhan, China The number of cases has been rapidly growing since then Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19 However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply Because the virus affects an infected patient\'s lung, interpreting images obtained from Computed Tomography Scanners and Chest X-ray Radiography (CXR) machines can be an alternative for diagnosis However CXR interpretation requires experts and the number of experts is limited Therefore, automatic detection of COVID-19 from CXR images is required We describe a system for automatic detection of COVID-19 from CXR images It first segmented images to select only the lung The segmented part was then fed into a multiclass classification module, which worked well with samples obtained from various sources, which had different aspect ratios, contrast and viewpoints The system also handled the unbalanced dataset-only a small fraction of images showed COVID-19 Our system achieved 92% of F1-score and 88 1% Marco F1-score on the 3rd Deep Learning and AI Summer/Winter School Hackathon Phase 3-Multi-class COVID-19 Chest X-ray challenge public leaderboard © 2020 ACM
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