Lung Cancer Detection by integrating U-Net and CNN

– Lung cancer remains a major global health concern due to late-stage diagnosis and limited access to fast, reliable screening tools. This research presents an integrated deep learning framework for automated lung cancer detection using CT scan images. The proposed system combines U-Net for precise segmentation of lung regions with a CNN-SVM hybrid model for accurate binary classification. Preprocessing steps such as resizing, normalization, and augmentation enhance model generalization, while the Adam optimizer and EarlyStopping techniques improve training efficiency. Evaluation results demonstrate high accuracy and stable training behavior. To support practical deployment, a Streamlit-based web application was developed, enabling real-time prediction from uploaded CT images. The framework offers a scalable and accessible solution for improving early lung cancer detection and aiding clinical decision-making.  .