Lung Cancer Classification using Adaptive Correlation Enhanced Active Contour Model and DenseEnsembleNet
Abstract Accurate lung cancer classification is essential for efficient treatment scheduling and positive patient outcomes since lung cancer is a prevalent and deadly condition. In this study, offered a cutting-edge method for classifying lung cancer that combines deep learning, feature extraction, and image processing advances. We commence by enhancing lung cancer images through advanced denoising techniques, augmenting the dataset via Generative Adversarial Networks (GANs) to bolster model generalization. Subsequently, we

