  The most deadly and life-threatening disease in the world is lung cancer.
Though early diagnosis and accurate treatment are necessary for lowering the
lung cancer mortality rate. A computerized tomography (CT) scan-based image is
one of the most effective imaging techniques for lung cancer detection using
deep learning models. In this article, we proposed a deep learning model-based
Convolutional Neural Network (CNN) framework for the early detection of lung
cancer using CT scan images. We also have analyzed other models for instance
Inception V3, Xception, and ResNet-50 models to compare with our proposed
model. We compared our models with each other considering the metrics of
accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the
model's performance, we observed that CNN outperformed other models and has
been shown to be promising compared to traditional methods. It achieved an
accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.
