Lung Cancer Prediction Using CNN And Transfer Learning

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early and accurate detection playing a crucial role in improving patient survival rates.Convolutional neural networks (CNN) and transfer learning technique are among the deep learning algorithms used for automatic classification of lung cancer disease,Such as Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal lung tissue.This paper uses deep learning techniques to develop a robust lung cancer classification model using chest CT images, which can be used to automatically achieve better accuracy.In this system propose a framework for classifying multiple lung diseases using the Xception and Mobilenet CNN algorithm and offering preventive measures based on disease predictions.In this model, trained on a lung cancer dataset from Kaggle, demonstrates high accuracy in experimental results.This approach ensures reliable disease identification and actionable prevention strategies.The automated classification system has the potential to assist radiologists and clinicians in diagnosing lung cancer more efficiently, reduce diagnostic variability, and support clinical decision-making processes.Future work will focus on expanding the dataset and integrating additional imaging modalities to further refine model performance and generalizability across diverse clinical settings.