Multi Disease

Multi-disease classification research aims to develop accurate and efficient methods for simultaneously diagnosing multiple conditions from various data sources, such as medical images, social media posts, and wearable sensor data. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often incorporating techniques like transfer learning, knowledge distillation, and attention mechanisms to improve performance and address data imbalances. These advancements hold significant promise for improving diagnostic accuracy, accelerating disease detection, and personalizing healthcare, particularly in resource-constrained settings.