Lung Cancer Detection

Lung cancer detection research intensely focuses on improving the accuracy and efficiency of diagnosis, primarily leveraging advanced image analysis techniques from CT scans and chest X-rays. Current efforts employ deep learning models, including convolutional neural networks (CNNs) and transformer architectures like Swin Transformer, often enhanced by optimization algorithms and multi-modal data fusion (combining imaging with clinical and genomic data). These advancements aim to improve early detection rates, leading to better patient outcomes and potentially reducing the high mortality associated with lung cancer.