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.