Lung Cancer detection Using Swin UNetTRansformers method

Lung cancer remains a significant global healthchallenge, with early detection playing a critical role inimproving patient outcomes. In this paper, we propose a noveldeep learning framework for lung cancer detection leveragingthe Swin-UNetR architecture. We begin by preprocessing themedical images to enhance contrast and remove noise,followed by patch-based representation to facilitate efficientprocessing with Swin-UNetR. The Swin Transformer blockswithin the architecture enable the model to capture intricatespatial dependencies and long-range contextual informationcrucial for accurate nodule detection. We evaluate theproposed framework on a comprehensive dataset comprisingdiverse lung imaging modalities, including CT scans. Ourexperimental results demonstrate the effectiveness of Swin-UNetR in accurately detecting lung cancer nodules,outperforming existing state-of-the-art methods in terms ofboth detection accuracy and computational efficiency

Lung Cancer detection Using Swin UNetTRansformers method Keywords:

lung cancer , deep learning , Transformerlearning , Swin-UNetR