Uncontrolled cell proliferation in lung tissues is what is known as lung cancer. Early Lung Cancer detection may hold the key to the disease's recovery. This research examines non-invasive techniques to aid with nodule detection. From the Computer Tomography (CT) pictures, it provides a Computer Aided Diagnosis System (CAD) for the early diagnosis of lung cancer nodules. The use of CAD systems aids radiologists in providing more accurate diagnoses when interpreting images. This method's main objective is to develop a CAD system for classify the nodule and using computed Tomography images to classify the lung cancer. In order to determine the optimal treatment plan for patients and their possibility of survival, convolutional neural networks can more quickly and reliably recognize and classify various kinds of lung cancer. The benign tissue, adenocarcinoma, big cell carcinoma, and squamous cell carcinoma are all considered in this work.