Lung cancer continues to be one of the prominent types of cancer leading to mortalities globally, and early and accurate diagnosis is crucial. This paper discusses the creation of a stable CNN that can be used for the diagnosis of lung cancer or presence of tumor. Based on the large number of labeled lung images, the CNN structure is designed to be highly accurate and fast. The model receives comprehensive training and cross-checking where the deep learning technologies such as data augmentation, transfer learning, and regularization methods are employed to improve the model’s performance. For the proposed CNN, results show high accuracy, sensitivity, and specificity, while also outperforming popular methods and other proposed models. Also, the visual representation of the network helps in the analysis of the findings and making decisions based on clinical considerations. Lastly, there is a focus on how deep learning presents a great opportunity for the screening and also the diagnosis of lung cancer, especially if lung cancer is at its early stages.