Lung Cancer Screening

Lung cancer screening aims to detect lung cancer early, improving patient outcomes through timely treatment. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), vision transformers (ViTs), and hybrid architectures to analyze CT scans and other data (e.g., blood tests, metabolomics) for improved diagnostic accuracy and risk prediction. These models are being refined to address challenges like data heterogeneity and bias, enhancing generalizability and reliability. The ultimate goal is to develop accurate, accessible, and cost-effective screening methods that reduce lung cancer mortality.