Disease Detection Model

Disease detection models leverage machine learning to diagnose illnesses from various data sources, aiming for faster, more accurate, and accessible diagnoses. Current research emphasizes the use of deep learning architectures like convolutional neural networks (CNNs), transformers, and hybrid models incorporating both, often trained on large datasets including medical images (X-rays, MRIs), physiological signals from wearables, and electronic health records. These advancements hold significant promise for improving healthcare by enabling earlier disease detection, personalized treatment strategies, and more efficient resource allocation, particularly in areas with limited access to specialists.