Lung inflammation is caused by the development of cancer cells. As the frequency of cancer rises, men and women are dying at a higher rate. With malignancy, cancerous cells multiply uncontrollably in the lobes. It is impossible to prevent lung cancer, but we can lower its associated risks. Early detection of lung can- cer can considerably improve a patient’s chances of survival. Patients with lung disease are more likely to be chain smokers. Several classification methods were applied to assess lung cancer prediction, such as the Deep CNN algorithm and Deep CNN, with the Final Layer as Machine learning. The first Deep CNN model defined this accuracy. However, the second model, Deep CNN+SVM, is the best mode2 defined. The accuracy is 98.61%. The primary purpose of this research paper is to identify lung cancer early. Throughout this publication, we identified two models: model 1 and model 2. Depending on this technique, Model 1 provides a new categorization method. With such a 98.61% accuracy, the support vector machine is the most accurate, whereas model 2 is the most accurate compared to model 1, which has a 98.4% accuracy.

CT, Deep CNN, Lung cancer, Machine Learning, SVM