Covid 19 Diagnosis Competition

The COVID-19 Diagnosis Competition (COV19D) focuses on developing and evaluating AI-powered methods for detecting COVID-19 and assessing its severity from chest CT scans. Research emphasizes the use of deep learning models, particularly 3D convolutional neural networks (CNNs) like ResNets, EfficientNets, and Swin Transformers, often incorporating techniques such as lung segmentation, ensemble methods, and attention mechanisms to improve diagnostic accuracy. These competitions drive advancements in medical image analysis, offering potential for faster, more accurate, and less resource-intensive COVID-19 diagnosis in clinical settings. The results highlight the effectiveness of various deep learning architectures and data augmentation strategies in improving the performance of COVID-19 detection and severity assessment systems.