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Corona-Net

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Title
Corona-Net
Subtitle
Fighting COVID-19 with Machine Learning
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130
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CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
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Abstract
Identified in December 2019, the novel Coronavirus has infected 2.7 million worldwide, and claimed the lives of 0.2 million. Amidst this deadly pandemic, I started my open source project, Corona-Net, in the hopes of contributing to the global fight against the Coronavirus. Corona-Net is a 3-part project dedicated to the classification, binary segmentation and multi-class segmentation of COVID-19. I first leverage the EfficientNet model for COVID-19 diagnosis, then utilise and refine the U-Net architecture for both binary and 3-class (ground-glass, consolidation, pleural effusion) segmentation of COVID-19 symptoms, through inference on the COVID-19 CT segmentation (chest axial CT) dataset. Through Corona-Net, I aim to develop a reliable, visual-semantically balanced method for automatic COVID-19 diagnosis, as well as extend an invitation to all to collaborate and stand together against this pandemic.