Customized Deep Learning Framework with Advanced Sampling Techniques for Lung Cancer Detection using CT Scans

Lung cancer is a devastating disease that kills around five million people annually, making it a major global threat. Timely recognition is critical to improving patient survival. With a focus on pulmonary nodules in medical imaging, CT scans are essential for the screening and identification of lung cancer. This paper introduces a customized deep-learning framework for lung cancer detection using CT Scans. To achieve an accurate and efficient early-stage lung cancer diagnosis, the model makes use of a large dataset of chest CT images for training and validation. The experimental results show that the model outperforms state-of-the-art methods for lung cancer categorization. The model provides a trustworthy and efficient means of early detection of lung cancer, which has the potential to revolutionize the field of lung cancer diagnostics. The deep framework outperforms existing approaches and highlights the need to apply transfer learning to medical image analysis. Because of the technique's extraordinary effectiveness, lung cancer research may be significantly impacted, and improvements in early diagnosis and therapy may result.