Early Detection

Early detection research focuses on developing methods to identify diseases and anomalies at their earliest stages, improving treatment outcomes and resource allocation. Current efforts utilize diverse machine learning models, including deep convolutional neural networks (CNNs), graph convolutional networks (GCNs), recurrent neural networks (RNNs), and hybrid quantum-classical approaches, often applied to multimodal data such as medical images, sensor readings, and patient-reported symptoms. This field is significantly impacting healthcare, agriculture, and cybersecurity by enabling faster, more accurate diagnoses and proactive interventions, ultimately improving patient care, crop yields, and system security.