Chest X Ray Classification

Chest X-ray classification uses machine learning to automatically detect and diagnose diseases from chest X-ray images, aiming to improve diagnostic accuracy and efficiency. Current research focuses on addressing challenges like data scarcity, domain shift (variations between datasets), and noisy labels, employing various deep learning architectures such as DenseNet, ResNet, Vision Transformers, and Graph Neural Networks, often incorporating techniques like self-supervised learning, transfer learning, and attention mechanisms. These advancements hold significant potential for improving healthcare by assisting radiologists, enabling faster diagnoses, and potentially improving access to care in resource-limited settings.