Disease Classification

Disease classification research aims to develop accurate and efficient methods for identifying diseases using various data sources, primarily medical images and other patient data. Current efforts focus on leveraging deep learning models, particularly convolutional neural networks (CNNs) and transformers, often incorporating techniques like transfer learning, multimodal fusion, and contrastive learning to improve performance and interpretability. These advancements hold significant promise for improving diagnostic accuracy, accelerating disease detection, and potentially personalizing treatment strategies across diverse medical domains, from ophthalmology and oncology to agriculture and livestock management.